Case
A Product Analyst for a global fintech: helping a new Head of Analytics build the hiring process
We found a Product Analyst and helped a new Head of Analytics build the search strategy, selection stages and offer
A global fintech company in online trading (700+ employees, 22 million clients in 120+ countries) was looking for a Product Analyst. During the search the hiring manager changed: the new Head of Analytics was a strong specialist but had no hiring experience. Over 6 months in 2025, NEWHR found a Product Analyst and, together with the new head, built the whole process: the search strategy, the selection stages and the offer.
Request and constraints
NEWHR has been working with this client for 6 years. During that time we have brought 12 narrowly specialized lead-level specialists into the team: Data Architects, ML Engineers, Data Scientists, BI Analysts with strong expertise in A/B tests, risk assessment and anti-fraud systems. For data roles the client comes only to NEWHR, without involving internal recruitment.
The 2025 vacancy seemed simple. But during the selection process the hiring manager changed: the Head of Analytics role went to a strong technical specialist who was new as a manager and had no hiring experience. A classic Product Analyst search turned into helping the new head through every stage of hiring.
- Experience working with data: analysis, finding insights
- Python and/or R for processing and analysis, data visualization tools
- A/B testing experience, knowledge of statistical methods and metrics
- Strong communication: the ability to explain analysis results to both technical specialists and business stakeholders
- Understanding of data engineering fundamentals
- Understanding of the specifics of financial data and metrics
- Experience working in a multidisciplinary team
- Initiative, willingness to learn new tools independently
- English, the company's main working language
Why it was hard
A closed industry
Minimal public information is a feature of trading. The vacancy is harder to “sell” to cold candidates: there are few striking cases and ambitious tasks in the public domain.
Office only during probation
The company has a lot of sensitive data, so for the first 3–6 months (depending on the position) an employee works from the office. The offices are not in the countries most popular with IT people, and the prospect of relocation narrowed the candidate pool.
A long selection process and a high bar for culture fit
The analytics team is small, and hiring is rare and very selective. A candidate has to be proactive, think in business metrics rather than technical tasks, initiate analytics projects on their own and see them through to results.
A change of hiring manager during the search
The new Head of Analytics needed time and help to “get a feel” for the market, understand the candidates and develop his own approach to hiring.
“Inexperienced hiring managers tend to move as if on rails, following the rules that already exist. Experience brings more freedom. And it does not matter at all how good the new manager is: hiring is a separate skill that takes practice.
For example, where one manager will search exclusively for a senior, another will hire a solid mid-level specialist plus an intern to save money and time. Because they understand when it is important to focus on finding a ‘star’ employee, and when it is more cost-effective and faster to bring a mid-level specialist's expertise up to the required level.
If this understanding is not there yet, an experienced recruiter will help by sharing expertise and suggesting non-obvious options.”
Oksana Prutyanova, Head of Data & AI Recruitment NEWHR
How we searched
The search was led by Oksana Prutyanova, Head of Data & AI Recruitment NEWHR. She has almost 20 years of experience in recruiting: she knows how the business processes of leading IT companies work, why candidates leave some teams for others, and how management and analytics work at different companies.
Together with the hiring manager, we drew up a list of source companies
Oksana helped the client formulate the vacancy and choose the industries to search in.
Obvious sources
Financial organizations and fintech, banks, payment systems, other online trading platforms and investment services.
Less obvious sources
E-commerce, marketplaces and product IT companies that actively test hypotheses, optimize sales funnels and make decisions based on BI. Gamedev, where player psychology is similar in many ways. Insurance companies: they work with large data sets to assess risks and personalize offers.
We rebuilt the selection stages for senior candidates
Next, together with the client, we formulated the candidate assessment criteria, the stages and their content.
“At the start of our work, the hiring manager was convinced that applicants could be selected only on the results of a take-home assignment that takes many hours. But with senior candidates this often does not work: they will not spend several evenings on a test task.
We suggested introducing flexibility and in the end arrived at this option: the candidate chooses whether to do the test task at home or take a live coding section right during the interview.
We also changed the order of stages for meetings with senior candidates, starting with a presentation of the company itself, the team and the position's tasks.”
Oksana Prutyanova, Head of Data & AI Recruitment NEWHR
We “sold” a closed company through its processes and feedback
Thanks to the long partnership, Oksana knew how to talk about the vacancy to candidates despite how closed the industry is. Besides the financial side, she emphasized well-established internal processes, predictable growth and honest, structured feedback after interviews, a quality that is rare among employers and that candidates value.
The recruiter's task here is to understand which opportunities of the role match a particular candidate's professional needs.
“An agency recruiter stands, as it were, ‘above the fray’. On the one hand, they dig down to the candidate's real motivation; on the other, they see the employer's strengths, which are not always obvious even to the hiring manager.
If I understand that a candidate is ambitious, and I know that their current company is ‘guilty’ of micromanagement and slow growth, I will be sure to highlight our client's advantages in this area during the conversation. Naturally, without making anything up or giving promises that cannot be kept.”
Oksana Prutyanova, Head of Data & AI Recruitment NEWHR
How we established a candidate's motivation
Through knowledge of the market
NEWHR recruiters know the reputation of source companies and of individual teams within them.
Through the information footprint
A public profile, conference talks and publications show a candidate's interests and ambitions well.
At screening and later interviews
A NEWHR interview is a detailed conversation about what interests the candidate, what drives them and why they left previous companies.
“It is telling how a candidate builds the logic of their answers. Some emphasize management qualities, for example that they hired or optimized a team. Some go straight to business value: how much money they saved or brought in. Some think in projects: they initiated a new feature, etc. All of this lets you understand what drives the person.”
Oksana Prutyanova, Head of Data & AI Recruitment NEWHR
What experience from earlier searches for this client suggested
Over 6 years of working with the client, NEWHR has accumulated solutions that go beyond full-time hiring. Two examples from other searches for the same company:
“With this same client (but within other searches) we had cases where I realized that hiring a permanent employee was not practical or not possible for the company. And we found another way!
For example, that was the case when our client was implementing complex machine learning models. Instead of searching for an expensive specialist with very narrow expertise on a permanent basis, I brought the company a consultant. He developed targeted risk assessment models tailored specifically to their business. After that, the task was picked up by a more broadly skilled ML engineer whom we found for the company's staff, saving it significant money.”
Oksana Prutyanova, Head of Data & AI Recruitment NEWHR
“Within another search, a candidate received offers from our partner and from another large tech company at the same time, and chose the latter. I knew from experience that she would not fit that company's culture, and I suggested that the client give her the chance to return to the hiring process later. The hiring manager agreed.
And that is exactly what happened: she left that company during her probation period and has now been successfully working for our client for several years.”
Oksana Prutyanova, Head of Data & AI Recruitment NEWHR
Result
Over six months (February — August 2025), NEWHR found a Product Analyst. Together with the new Head of Analytics we went through every stage of hiring: we formulated the vacancy, set the timeline and criteria, developed the search strategy and the list of source companies, helped assess candidates' soft skills and culture fit, set up communication with candidates and helped make an attractive offer to the finalist without inflating the hiring budget.
The new head settled into the hiring manager role, developed his own approach to team selection and gained an understanding of the market.