Analytics Leadership in PE Portfolios: Timing the Right Hire

A Head of Analytics can play an important role in translating data capability into measurable value creation inside a portfolio company. For operating partners, the question is not simply when to hire, but how analytics leadership fits within broader portfolio design and value creation priorities.

Across private equity portfolios, firms are investing in data cube architecture, KPI standardisation, and portfolio-wide reporting infrastructure. Even where central infrastructure exists, execution inside each company still depends on leadership capability at the operating level.

Introduced at the right stage, analytics leadership can improve decision quality and strengthen operating discipline. Introduced without clarity on mandate or readiness, it can add structure without measurable impact.

On this page

• Why analytics leadership matters in portfolio design
• When to hire a Head of Analytics
• What operating partners should look for in analytics leadership
• Permanent vs interim leadership across the hold period
• How to align analytics leadership to portfolio infrastructure
• What this means for operating partners

Why analytics leadership matters in portfolio design

Many PE firms are building portfolio-level reporting environments, data cubes, and standardised KPI frameworks. These initiatives improve visibility across assets and support fund-level performance management.

However, portfolio infrastructure does not replace operating leadership inside each company.

At company level, analytics leadership is responsible for:

• Translating portfolio KPIs into operating priorities
• Aligning analytics work to commercial and operational decisions
• Ensuring local execution reflects the investment thesis

Without this execution layer, reporting can improve while operating decisions remain unchanged.

For operating partners, the question becomes: where does leadership accountability sit between portfolio-level data architecture and company-level execution?

When to hire a Head of Analytics

Private equity ownership alone does not automatically require a Head of Analytics. The role becomes relevant when growth stage and operational complexity create coordination challenges.

When analytics leadership becomes necessary

Hiring before these signals emerge can create unclear mandates. Waiting too long can leave analytics reactive rather than integrated into operating cadence.

What operating partners should look for in analytics leadership

In PE-backed environments, analytics leadership must operate at the intersection of execution and value creation.

Key attributes typically include:

Commercial orientation

Leaders must understand how analytics connects to revenue quality, margin expansion, cash flow, and operating efficiency.

Stakeholder credibility

The role requires influence across CFOs, COOs, commercial leads, and board-level stakeholders.

Scale-stage awareness

Experience operating in growth environments is important. The balance between structure and speed shifts over the hold period.

Across Harnham’s work with PE-backed companies, effective analytics leaders are rarely defined by technical depth alone. Impact depends on their ability to prioritise use cases tied directly to value creation.

Permanent vs interim leadership across the hold period

Not every business requires a full-time hire immediately.

Market data suggests that permanent hiring remains the dominant model, but operating partners often use interim leadership early in the hold period to clarify scope before scaling.

How to align analytics leadership to portfolio infrastructure

As firms invest in portfolio-wide data cubes and reporting layers, clarity of responsibility becomes critical.

Operating partners should distinguish between:

• Portfolio infrastructure – centralised reporting, KPI standardisation, fund-level analytics
• Company execution – embedding analytics into pricing, operations, forecasting, and daily decision-making

A Head of Analytics operates primarily in the second category. Even in well-instrumented portfolios, execution risk remains unless leadership translates reporting insight into operating change.

What this means for operating partners

Analytics leadership should not be mandated uniformly across all portfolio companies. Instead, decisions should be guided by stage, complexity, and value creation priorities.

Operating partners may consider:

• Should analytics leadership be introduced in every asset, or only those with complexity thresholds?
• Is interim leadership appropriate early in the hold period while infrastructure stabilises?
• How do we avoid over-hiring before data foundations are mature?
• How will leadership impact be measured against the value creation plan?

Success is rarely defined by dashboard delivery. It is measured by improvements in decision quality, operating consistency, and alignment to the investment thesis.

Many firms use market data, such as Harnham’s Data & AI Hiring Guide, to sense-check expectations and retention risk across the hold period.

How Harnham supports analytics leadership across PE portfolios

Across private equity portfolios, the challenge is less about recognising the importance of analytics and more about structuring leadership appropriately across assets.

Harnham works with operating partners and portfolio leadership teams to:

• Define analytics leadership mandates aligned to value creation plans
• Benchmark role scope and seniority at different growth stages
• Introduce interim or permanent leadership in line with hold-period priorities

Our focus is on ensuring analytics leadership supports portfolio design rather than adding complexity without clear accountability.

For firms reviewing analytics leadership needs, you can explore Harnham’s analytics hiring capabilities or speak with our team for a market-led discussion.

Last updated: 2026

Written by Harnham’s Analytics & AI leadership team.

The post Analytics in Private Equity: Driving Portfolio Value appeared first on Harnham.

With tech unemployment hitting 1.8 per cent in the US during August, and businesses struggling to find specialists in key areas such as cloud, Artificial Intelligence (AI) and Machine Learning (ML), employers need to work hard to fill vacancies and build strong teams. For those looking for top candidates during the hiring frenzy, the pressure is on to stand out as a top employer in such a competitive landscape.

Our most recent Data & AI Salary Guide has given us a strong insight into the market, including a sense of candidate sentiment in the data space and what they value most when looking for a new role. So, we want to give you the inside scoop of how to get ahead of the surge.

Is your salary competitive?

Companies should see salaries as an investment rather than an expense. The salary you pay to your employees needs to be comparable to the market, for example – it should match the standard being offered by companies in a similar industry or geographical area to be deemed attractive by a potential candidate.

Last year saw salaries rise to an unrealistic level which led to mass redundancies, so the goal is to offer an attractive salary without going overboard. Our salary guide found that 26 per cent of candidates rated uncompetitive salaries as the top reason for leaving a job and 21 per cent were seeking a salary increase. Talented employees know their worth and they will only buy into a company if they feel the salary matches the skills and value they will add to the organization. Salaries not only attract talent, but they act as a retention tool whilst keeping teams motivated to achieve their goals. Research shows companies who increase their base pay by 10 per cent will make workers stay at the company for longer.

That said, it’s not always easy to know what a competitive salary offering looks like. That’s why this year we are offering free personalized salary insights reports that businesses can download to inform their hiring strategy, help with benchmarking and ensure they are hiring in line with industry trends.

Offering hybrid and remote working

In 2022, remote work accounted for over a quarter of paid full-time workdays in the United States. Flexible working arrangements have become a non-negotiable benefit for many candidates, with advantages such as a reduced commute, better work-life balance and even increased productivity, are not going to be easily given up.

According to a Buffer study as cited in Forbes, 98% of workers would like to work from home at least some of the time, confirming that it’s a benefit that cannot be skirted over if employers hope to attract top quality applicants. Hybrid working arrangements also open the door to a wider pool of talent outside of core cities, as many commutes become more manageable when only required for a few days a week.

Ensure your benefits are meeting demand

Benefits can be a deal breaker for employees and with some companies offering gym memberships, private healthcare, wellness programs, flexible hours and financial incentives, it’s crucial to include them in your employee packages. In fact, 69 per cent of employees would choose one job over another if it offered better benefits. Clearly, non-financial benefits have the potential to be the deciding factor that sways an individual to consider your company.

The key benefits highlighted by candidates in our most recent salary guide were remote working, closely followed by bonuses and health insurance, which gives organizations an idea of where to start.

Collaborate with recruitment agencies

Partnering with reputable recruitment agencies that specialize in your industry could be a way to cut through the noise and access a larger talent pool. Agencies not only benefit the organization in terms of time and resource savings, but they give an extra layer of expertise in finding a suitable match for the role. Data recruiters know the industry inside out and their experience flags any warning signs of a costly error, so you can be confident of a high-quality hire.

Finding talented individuals that can form strong teams is no mean feat, but there are tried and tested strategies to weave into your recruitment process that can help. In the meantime, Harnham can help with your hiring needs, our consultants understand the challenges faced by business leaders as they look for quality talent to fulfil crucial data roles –and have a comprehensive view of the jobs market landscape – get in touch today.

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No matter where you stand on your preference for freelance versus permanent roles, there has certainly been a swell in the number of contracting Data & Analytics professionals in markets across the world. For example, in our New York office, in the first three months of 2023, we have already seen more than half the number of new temporary contract positions come up as were listed during the whole of 2022.

What’s driving this growth? While there are a number of intermingling factors at play, here are three trends that Jose Gomez, Managing Consultant for Harnham in New York, has noticed in the current contract market.

Testing the Waters

Gomez considers ‘contract-to-hire’ as the number one recruitment trend in the interim sector at the moment. He describes the contract-to-hire method as an opportunity for both parties to ‘try before you buy’, by entering into a contract that initially lasts for three to six months but can be converted into a permanent job at any time.

For an employee, this means they can test the water in an unfamiliar firm to see whether they enjoy the job and workplace environment. For the employer, it is an invaluable way to try out a promising candidate for a specific project but postpone the administrative and financial implications of a permanent job offer, until they are certain they’ve found the right fit. A short-term commitment from both sides can ultimately lead to a long-term solution that suits both the employee and employer.

A Project-Based Approach

Another significant trend that is driving contracting markets is project urgency. Regardless of wider global events and uncertain financial markets that may influence whether a business commits to growing its headcount, there are a number of time-sensitive projects that still need to go ahead. These tend to have a clear start and finish date mapped out.

Bringing in professionals on a short-term basis who are accustomed to hitting the ground running without the employer needing to commit to the paperwork and costs of a permanent hire, means these urgent projects can still be completed by their deadlines.

With the growing importance of Data and Analytics for many organisations, this is translating into a tangible uptick in the number of contracting positions we are filling for clients, with many looking to hire into multiple project-based roles. This approach also means that employers benefit from the experience of contractors who have worked on a diverse range of projects across different firms.

The Impact of Evolving Technology

A third trend powering demand for contract work in the sector is Artificial Intelligence (AI). Everyone has heard of ChatGPT, but what is it all about? For Harnham’s specialist recruitment consultants, AI is our bread and butter and we are seeing a growing call for Machine Learning (ML) engineers who can work in numerous programming languages and data models.

Given the current trajectory of the Data & Analytics market, we expect to see all three trends continue to grow going forwards, leading to lots of opportunities for contract work.

Should someone currently in the ‘permanent’ camp consider a switch? We find that contracting work does not fit a specific demographic, it’s a personal lifestyle choice. For some, the flexible, freelance lifestyle is highly desirable and offers many benefits, but it is quite different from the permanent approach and therefore won’t suit everyone.

Before placing any contractor into a role, we set up an agreement between the employer and contractor to cover all eventualities. Harnham’s recruitment consultants are highly experienced at matching the right contractors with the right businesses and we provide contracts on a part-time and full-time basis.

The great advantage of this type of employment is that recruitment can be extremely quick and efficient. We have seen the process from interview to job offer to signing agreements happen within two days, which is an extremely fast turnaround. And whilst contractors generally don’t receive the same company benefits as permanent staff, at Harnham we do offer benefits to the contractors we have on our books.

Interested in hiring a data contractor for an upcoming project or expanding your team with a permanent data professional? Our expert team knows the market inside out, and how to reach the best talent out there, get in touch today.

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Throughout 2022, interest rates soared in an effort to curb inflation, which drove up consumer prices and the cost of borrowing. Because of this, experts and investors warned that in 2023, the U.S. economy would most likely experience a slowdown, and some even warned of an imminent recession.

But with 2023 well underway, have the doom and gloom predictions about the U.S. economy really come to fruition? In other words, what’s the current state of the U.S. economy?

According to several key economic trackers, the U.S. economy has been faring a lot better than anticipated. So much so, that some experts are debunking the recession rumors altogether. Below, will touch on some of the key economic trackers that explain why economists are feeling optimistic—and why you should be, too.

The Stock Market Has Rallied

According to economic researcher Brad Comincioli, stock prices are a key economic indicator because they “reflect expectations about profitability, and profitability is directly linked to economic activity.”

Therefore, if a key stock market index like the S&P 500 is up, that typically means investors are feeling optimistic about economic growth. As of now, the S&P 500 is up 6.67% year-to-date, which is an increase of more than 14% from its latest low in mid-October last year.

This means we’re officially out of the bear market—but according to Christopher Harvey from Wells Fargo, we haven’t quite reached bull market status either. According to Harvey, investors should “expect some giveback, but not a sharp near-term reversal.”

So, while things aren’t quite bouncing back as they did in 2020, the U.S. stock market is in relatively good shape currently.

Unemployment Remains Low

Unemployment rates are also a good indicator of how a country’s economy is faring.

Low unemployment rates are a good sign, and according to the latest report released by the U.S. Bureau of Labor Statistics, the U.S. unemployment rate has dropped to 3.4%—that’s not only lower than the market expectation of 3.6%, but it’s also the lowest level it’s been since 1969, more than 54 years ago.

Job growth was also high last month, with more than a half million jobs created in January alone. According to the U.S. Bureau of Labor Statistics, this job growth was “led by gains in leisure and hospitality, professional and business services, and health care.”  

Gross Domestic Product (GDP) Growth Expected

Because GDP measures the value of its goods and services produced within a certain time period, it’s an excellent indicator of how a country’s economy is doing. As of the latest figures, GDP in the U.S. has continued to increase—in Q4 2022, real GDP in the U.S. grew by 2.9%, and overall growth for the year was 2.0%.

And predictions from the International Monetary Fund expect more growth on the horizon, meaning the U.S. could very well avoid the recession that many experts were expecting.

Why Now Is the Time to Hire

While the state of the U.S. economy is a lot better than the news is making it out to be, it’s important to acknowledge that the economy has certainly seen brighter days.

However, there’s a silver lining to this luke-warm economic climate we’re currently in the midst of—for companies and business leaders, this period of calm is a great time to sit back, reprioritize, and rebuild your teams to make sure your company is set up for success when things start to inevitably pick up.

If you don’t hire now, you could be scrambling for talent in the future, or battling with other companies for the best candidate, which is when salaries start to hike up. So, to avoid a bidding war, start hiring now.

In search of data talent? Get in touch with one of our expert U.S. consultants.

For our West Coast Team, contact us at (415) 614 – 4999 or send an email to sanfraninfo@harnham.com.

For our Arizona Team, contact us at (602) 562 7011 or send an email to phoenixinfo@harnham.com.

For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.

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For businesses who want to improve and scale their business much faster, there is an emerging profession within the data industry that is already being led by Big Tech and tech startups. Enter Machine Learning Operations (MLOps).

You may already have someone on your team who is working within the parameters of what this role can do for your business, but what if you structured your data team with a professional dedicated to the infrastructure that makes your business run more smoothly?

But before you can employ or even search for someone with such a specific skill set, it’s important to understand who you should be looking for, what they should be able to do, and how their efforts interact with your data team.

What’s driving the increase of MLOps for businesses?

When you know the right terminology to find the right person for the right role, that is when you can be confident in ensuring the infrastructure is being laid by those who understand its intricacies. As an emerging profession, one of the main trends we’ve seen is that of bringing on more Data Specialists in the last few years who can write high-level production code into production. Even pre-pandemic, organizations were hiring PhDs and master’s degree professionals with commercial experience who wrote these high-level algorithms that could identify unique segments of the market.

Regardless of the industry, there was a central question that evolved, which asked, “How do we scale from here”? and “How do we create a data platform that is able to go from 0 to 1”? But asking the question, how do we scale wasn’t the whole question when it came to this area of Machine Learning Operations (MLOps). If you flip the acronym and consider that all businesses have an internal operations department that is responsible for ensuring best practices, then it becomes Operations of Machine Learning. Then, from there, determining best practices to enable your business to scale faster ensures you’re more able to keep up with the competition.

Five Best Practices

MLOps is often labeled incorrectly. This is not a one-size-fits-all role and requires someone with deep knowledge of Python, Engineering, and Computer Science, and has worked in a business that has scaled.

• Deep knowledge of Python.
• Have experience bringing a company to scale.
• Create a machine learning platform that when new data comes in there is a change in the market, a change in product, a change in whatever your type of data is you have the infrastructure to scale something quickly.
• Ensure you have the right machine learning platform, or it will be difficult to get the most out of your Data.
• Make sure your Data is accurate and strong so your MLOps Engineers can get the most out of it.

MLOps Engineers should be excellent with Python, own the engineering side, and understand how all the pieces fit together and be able to create, maintain, and deploy the models. Companies are trying to get there, but so many of the hires for a field this new are first hires. Businesses need someone who understands the intricacies of the engineering of these platforms.

It’s important for businesses to understand what they’re looking for because even if you don’t have anyone with the title of MLOps on staff, you most likely have someone who is doing the role already regardless of title. Your goal, whether you realize it or not, is make your ingest Data faster, so it can be more usable, and you can learn more from it.

Deciding Between MLOps and Machine Learning? This May Help.

There are two sides to every opportunity and MLOps is no different. If you’re interested in seeing how your work directly impacts and affects the business, then MLOps may be the role for you. When you’re this specialized, there is increased visibility, increased monetary value, and though it may not be as creative as other endeavors within the Data team, you’re where the buck stops when it comes to ensuring the algorithms and Data work together to move the business forward.

A quick example: Imagine you are part of a team working to get a book written and in stores. The author is the Data Scientist who researches, writes, and creates. The Data or Software Engineer is the Editor who makes sure everything is clean before it goes out the door. The MLOps Engineer is the Publisher who gets the book into stores so readers can find and read it. A book isn’t much to good to someone if it’s not where they can access it. Same goes for Data. The Data Scientist, Engineers, and MLOps work together to scale the business each in their specific role.

Companies committed to tech whether a tech startup, Big Tech, or a business that knows it needs tech to stay competitive also know that adding MLOps to their Data strategy is an absolute must to stay on the cutting edge.

If you’re interested in MLOps, ML Engineering, Data Engineering, or Data Science just to name a few, Harnham may have a role for you. Being on the cutting edge of emerging Data industry trends is our specialty.

Check out our latest MLOps jobs or contact one of our expert consultants to learn more.

For our West Coast Team, contact us at (415) 614 – 4999 or send an email to sanfraninfo@harnham.com.

For our Arizona Team, contact us at (602) 562 7011 or send an email to phoenixinfo@harnham.com.

For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.

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Why? Imagine a patient sees a doctor to find out why they’re not feeling well. The doctor can’t quite pinpoint the illness and sends the patient for an MRI scan.  When the scan comes back, the doctor wants to confirm the diagnosis, and so sends the patient for another scan. But this time, the scan comes back with a completely different diagnosis than the one before. Multiple scans offering multiple diagnoses? 

How Computer Vision and AI are Assisting Healthcare Providers

Errors like this are costly to the patient and provider as they struggle to determine proper treatment. So, computer vision is becoming relied upon more and more helping overworked doctors make the correct diagnosis  the first time.

Add AI into the mix and not only is the technology being used in medical imaging, but robots are being used in surgeries as well. What if these latest technologies could determine COVID bacteria to help create vaccines? It can.

Using medical imaging analysis, the aim was to try to predict the next sequencing or evolution of the bacteria. If this could be done, along with determining its adaptability, researchers could predict what might be next and help create a vaccine. Advanced technology like this shaved months off the traditional route and massive amounts of data to get a vaccine to people quicker, than say a vaccine for Tuberculosis (TB). These are just a couple of examples.

What to Look for in the Next Five Years

It can be a little scary to think about what we might see in the next five years. If you think about computer vision outside the medical imaging space, there are many other ways robotics and AI are entering our daily lives:

• At the bank, a robot greets you and asks how they can help.
• Robots flipping burgers, making coffee, or building a salad.
• Robots in surgery offer a hands-off, non-invasive option for procedures. Robots are learning in real time, and learning from their mistakes.

This is the basis of an AI algorithm which is the machine continuously learning, and getting smarter and smarter. At the moment, we can shut them down, if needed. But what happens on the day we can’t?

But back to the medical imaging space. Computer vision and AI is ever-evolving and can help with a variety of diseases such as respiratory problems, cardiology, cancers, and radiology just to name a few.

There’s still a ton of research to be done, but here’s the thing. Imagine if the doctor’s could file through years worth of data to find an illness they hadn’t thought about since their school days, but that could help pinpoint their patient’s illness. Using medical imaging analysis they can. Not only will it save patients money and doctors’ time, but it will speed up treatments for the right diagnosis, and that’s good for everyone’s health.

It’s important to understand how these automatic systems can build upon and support doctors. Computer vision can make things much quicker, more accurate, and more efficient for a better patient outcome.

More complex problems arise every day. There are new diseases to treat no one has ever seen before. But when you combine medical imaging with AI, deep learning, and computer vision, the possibilities can go deeper than ever before.

Getting into the Field

If you’re wondering what it might take to enter this field, it’s a long road requiring the highest education available. Those individuals who have the background are most likely PhD candidates who have been in the space already for four of their five doctoral years. These professionals will have spent their time in research labs within a hospital system. They may not be doctors, but they are innovators in their field and of the world creating new products and services each day. These are the individuals always pushing to know how to make healthcare better.

There are a few schools who are ensuring the next generation of Data professionals within the robotics, AI, and computer vision fields get the best foot forward available. Carnegie Mellon offers an outstanding robotics school known as one of the best in the world. Its cost can be prohibitive requiring smart students who may not have enough funds to go to school in their home country, then come to the US for their robotics degree. Other top schools for robotics include the University of Michigan, the University of Texas, and the University of Georgia.

If you’re interested in computer vision, AI, NLP, data science, machine learning, or robotics just to name a few, Harnham may have a role for you. Contact one of our expert consultants to learn more.  

For our West Coast Team, contact us at (415) 614 – 4999 or send an email to sanfraninfo@harnham.com.
For our Southwest Team, contact us at (602) 562 7011 or send an email to phoenixinfo@harnham.com. 
For our Midwest and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.  

The post We’re Only Human: The Case for Computer Vision in Medical Imaging appeared first on Harnham.

Toni Morrison said, “if you’re not finding the book you want to read…you should write it.”

N.K. Jemisin’s short story collection, How Long ‘til Black Future Month? highlights some key Black tech pioneers that have come before. However, those stories don’t shed light on the pioneers who are in the trenches now, paving the way for future generations.

So, today, we wanted to highlight some of the world’s modern Black tech pioneers, whose contributions have helped shape the tech space as we know it today. This might not technically be a book like Morrison suggested, but it’s our way of documenting the stories we want to read.

12 Black Tech Pioneers: Then and Now

From the first PC to the first Gigahertz microprocessor to the Cloud, there is a human, and a name, behind the tech. That person may not be a household name you’d recognize, but without them, our technology would likely look quite different than it does now.

Below are twelve Black tech pioneers who are making a difference in today’s tech sector, and shaping the future of the tech industry. Some of their achievements include breaking into the highest levels of academia in Computer Science, making STEM fields equal to all children, and more.

1. Robert Frederick. Meet the technical co-founder of AWS. With 41 patents under his belt, it’s no wonder his work is the basis for many cloud services.
2. Marsha Rae Williams. Meet the woman who laid the foundational components for cloud services and cybersecurity with her work in Data queries and Data user experience.
3. Rediet Abebe. Meet the UC Berkley professor opening doors for black people in the artificial intelligence field as the Co-founder of Black in AI.
4. Latanya Sweeney. Meet the Harvard Kennedy School professor and founder of the Data Privacy Lab studying discrimination in facial recognition.
5. Mark Dean. Stepping back in time a bit, meet the chief engineer on the team that invented the first IBM PC and the first Black engineer to be named an IBM fellow.
6. Charlene Hunter. Meet the woman behind Coding Black Females and Black Codher Bootcamp, organizations focused on empowering Black women to enter the tech industry and support Black female developers.
7. Marian Croak. Meet the woman behind Voice Over Internet Protocol (VOIP) technologies which powers much of the software we use today.
8. Herman Chinery-Hesse. Founder of the largest software company in Ghana, SOFTtribe — affectionately known as the “Bill Gates of Ghana”.
9. Lisa Gelobter. Meet the woman behind tEQuitable, focused on ensuring employees and employers have a place to discuss diversity, equality, and inclusion as well as a place to address issues such as discrimination and bias for better inclusivity.
10. James E. West. Meet the man who co-invented the prototype of what would become the microphones we use today.
11. Ayah Bdeir. Meet the woman who has turned the erector set on its ear and is inspiring young people to become inventors. Her aim with littleBits is to make STEM activities accessible to all regardless of socioeconomic status by producing modular electronics and small magnets for prototypes.
12. Kimberly Bryant. Meet the woman behind Black Girls Code which provides education in coding, game design, and computer science to encourage young girls to get involved in the STEM field.

If you’re interested in Digital Analytics, AI, Data Engineering, Data Science, Machine Learning, or Robotics just to name a few, Harnham may have a role for you. Check out our current vacancies or contact one of our expert consultants to learn more. 

For our West Coast Team, contact us at (415) 614 – 4999 or send an email to sanfraninfo@harnham.com. 

For our Arizona Team, contact us at (602) 562 7011 or send an email to phoenixinfo@harnham.com.

For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.  

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