Specialities · Analytics
AI, analytics and data science
recruitment for financial
services and GCCs
From data scientists and machine learning specialists to predictive analytics leaders, Gen AI and Agentic AI talent and AI governance specialists, we help insurers, banks, consultancies and global capability centres hire the people who build, govern and scale modern data science and AI functions. The market is moving quickly, and finding professionals who combine technical depth, business understanding and leadership is getting harder. India is at the centre of this, and so are we.
What analytics and AI roles does EliteRecruitments recruit for?
EliteRecruitments recruits across the full AI, analytics and data science spectrum: data scientists and machine learning engineers, generative AI, LLM and Agentic AI engineers, MLOps and AIOps specialists, computer vision and NLP specialists, predictive analytics and deep learning talent, analytics engineers, the applied analytics domains from marketing and product analytics to risk, fraud, digital and decision analytics, AI governance and Responsible AI professionals, and analytics and AI leadership up to chief AI and chief data officer. It serves insurers, banks, consultancies and global capability centres, with deep demand in India’s GCCs, where data science and AI hiring is among the fastest growing in the market.
Since 2015
analytics has been a practice alongside actuarial
80%
screening ratio, candidates we submit get interviewed
85%
joining ratio, offers that become joiners
GCC led
where the fastest growing data science and AI hiring sits
Our analytics depth
From supporting function to the centre of competition
Analytics and AI has gone from a supporting function to the centre of how financial services firms compete, and the hiring market has not kept up. Demand for data scientists, machine learning specialists, predictive analytics talent and AI specialists now consistently outruns supply, and the gap is widest exactly where it matters most, in the experienced middle who can both build and lead.
For most organisations, data science is no longer a technology initiative. It is a business capability. Pricing, underwriting, claims, risk management, customer experience, regulatory reporting and AI adoption all depend on the strength of the analytics and AI function underneath them. That is why the people who build and run that function have become some of the most contested hires in financial services.
We have recruited in analytics since 2015, when we added it as a practice alongside actuarial, and we have placed heads of analytics, data scientists, predictive modellers, and the analysts and machine learning specialists who sit around them. That history matters now, because the firms scaling data science and AI teams want a recruiter who understood this space before it became the hottest part of the market, not one who arrived with the hype.
The analytics and AI roles we recruit
We recruit across the whole application layer
Data science and AI is not one job family. It spans the people who find insight in data, the scientists who build and train models, the AI and LLM engineers who put models into products, the specialists who deploy, monitor and govern them, and the leaders who turn all of it into decisions.
Gen AI and Agentic AI
Generative AI and Agentic AI engineers, large language model and LLM engineers, RAG engineers, prompt engineers and AI platform engineers, building on platforms such as OpenAI, Azure OpenAI, Google Vertex AI and Gemini, Anthropic, Databricks and Snowflake Cortex, often with LangChain and LangGraph. In financial services these are the people designing AI assistants, retrieval augmented generation over policy and claims documents, and agentic workflows that act on data rather than simply reporting it. Demand here has moved from experimental to core in a very short time, and the strongest candidates are scarce.
Data science and machine learning
Data scientists, machine learning specialists and deep learning practitioners, across predictive, prescriptive and decision modelling. In insurance and financial services this includes pricing and risk models, fraud and financial crime models, customer and propensity models, and the applied machine learning that increasingly sits behind them.
Predictive analytics and business intelligence
Predictive analytics specialists, data analysts, business intelligence developers, MI and reporting analysts, and data visualisation specialists, across decision, predictive, prescriptive and customer analytics. These are the people who turn data into something a business can act on, in functions from underwriting and claims to pricing, marketing, operations and finance.
AI and model governance
AI governance and Responsible AI leads, AI risk and model risk specialists, model validation and monitoring professionals, and explainable AI, AI ethics and AI compliance specialists. As regulation catches up with AI, these roles have gone from rare to essential, and many organisations are finding that a successful AI programme depends less on the model itself than on governance, explainability, monitoring and controls.
MLOps and AIOps
ML engineers, MLOps engineers and AIOps specialists who take models from a notebook to a governed, observed live service and keep them performing once they are there. This is the discipline that decides whether a promising model ever delivers value, particularly common in capability centres running machine learning and AI at scale, yet still poorly served by generalist recruiters.
Applied analytics
Analytics specialists who sit inside a specific business function and know its data and its questions in depth: marketing, customer, product, risk, fraud, digital and web, supply chain and operations, pricing and decision analytics, and customer insight. Function specific analytics is where a great deal of the real hiring volume sits, and it is exactly the depth generalist recruiters miss.
Computer vision and applied AI
Computer vision engineers, NLP specialists, and applied AI professionals who build models against images, documents and unstructured data, across image recognition, document intelligence, video analytics, fraud detection and insurance claims assessment. A specialist band that generalist recruiters rarely reach.
Analytics engineering
Analytics engineers and analytics developers who sit between the data platform and the business, building the trusted datasets, semantic layers and reporting that let an organisation make decisions at scale. Mandates commonly involve dbt, Power BI, Tableau, Looker and Microsoft Fabric. One of the fastest growing analytics roles and still poorly understood by generalist recruiters.
Data governance and quality
Data governance managers, data quality leads, data stewards, master data and metadata specialists, and data risk and control professionals. As organisations invest in AI, the quality, lineage and control of the underlying data has become a board level concern, and hiring in this area has grown accordingly. We treat governance as its own discipline, because it is one.
Data product and transformation
Data product owners and analytics and AI product managers who own a data or AI product end to end, and the analytics programme and project managers and transformation leaders who deliver data science change. We recruit them where they sit genuinely within an analytics, data science or AI function, not as generic product or project roles.
Analytics and AI leadership
Chief AI officers, chief data and chief analytics officers, heads of AI, heads of analytics and data science, VPs of analytics, directors of data science, and AI transformation leaders. The chief AI officer has moved from rare to common across banking and insurance in a very short time, and these are increasingly the roles that decide whether an AI and analytics function actually delivers.
For enterprise data platforms, cloud engineering and modern data architecture, see our dedicated Data Engineering and Cloud practice, so we cover both the analytics and AI layer and the platform beneath it.
Where the data and AI hiring is
India’s capability centres are the engine rooms
The biggest concentration of data science and AI hiring in the market right now is inside India’s global capability centres. Reported industry estimates in early 2026 put GCCs at roughly a third of all AI related hiring in India, with demand for AI specialists up more than threefold since 2024 and a data science and AI skills gap estimated at around 40 per cent of need. India’s GCCs are no longer back office support. They are increasingly responsible for enterprise AI platforms, AI governance, analytics products and advanced modelling, and they are the engine rooms where international firms build and run their data science and AI capability.
Five to six adjacent roles per AI specialist
What that means for hiring is specific, and it shapes how we recruit. The largest hiring volumes are often not the AI scientists themselves, but the machine learning specialists, MLOps and AIOps professionals, governance professionals and analytics specialists needed to operationalise AI at scale. Reported analysis suggests that for every core AI specialist, a capability centre needs five to six people in the surrounding analytics and AI roles, the people who turn a pilot into a governed, production system. The scarcest band is the experienced eight to fifteen year cohort who can both build and lead, and that is exactly the band our deep, mapping led process is built to reach.
Why capability centres come to us for analytics first
We help international insurers, banks and firms build and scale these analytics, data science and AI functions in their India centres, from the first senior hire through the wider team. It is one of the strongest areas of demand we see, and one where our combination of financial services depth and data science specialism is genuinely hard to match.
Analytics is often the first relationship that becomes a broader partnership. Because analytics and AI teams touch almost every business function, an analytics build inside a capability centre frequently leads on to actuarial, risk, audit and leadership hiring with the same client. So while analytics is one of our strongest practices in its own right, it is also one of the most common doors into a full talent relationship.
Analytics for fintech and insurtech
Blended briefs, across business and technology
Some of the fastest growing analytics and data teams sit inside fintech and insurtech businesses, and they hire differently. These firms want people who combine data science and AI skill with a real grasp of product, risk and growth, often spanning data science, product analytics, risk and fraud analytics, customer analytics, Gen AI and predictive analytics at once. The strongest candidates operate across both the business and the technology side, rather than in a single lane.
That is exactly the kind of blended brief we are built to fill. Because we cover analytics, AI, risk and actuarial together, we can find people who sit between data science and the functional side of an insurtech or fintech business, which a single discipline recruiter usually cannot.
Who we serve
Who we hire analytics and AI talent for
Insurers and reinsurers building analytics, pricing and AI capability. Banks and fintechs scaling data science, risk analytics and fraud and financial crime models. Consultancies that need specialist analytics and AI talent for client work. And, above all right now, the global capability centres building analytics, data science and AI functions in India for their parent companies around the world. Each hires differently: a bank’s fraud analytics team, an insurer’s pricing data science function, and a capability centre’s first machine learning build are three different problems, and we have worked across all of them since 2015.
Insurance
Analytics, pricing and AI capability across insurers and reinsurers.
Insurance industryBanking and FinTech
Data science, risk analytics and fraud and financial crime models.
Banking industryConsulting
Specialist data and analytics talent for client work.
Consulting industryGlobal capability centres
Data, analytics and AI functions built in India for parents abroad.
Global Capability CentresWhy our placements hold
Telling capability from presentation
Data and AI is a field where almost everyone now looks strong on paper. Tools and titles are easy to list, and AI has made polished CVs effortless to produce. The hard part, and the part that matters, is telling genuine capability from presentation. We assess whether a data scientist can actually frame and solve a business problem, whether a machine learning specialist has really built and shipped models at scale, and whether the person fits the team and the goals of their next move, before any profile reaches you.
That discipline is why our placements last. We screen at an 80 per cent rate and our candidates join at an 85 per cent rate, and since 2014 we have had to return fewer than fifteen fees because a placement left early. In a market where data professionals often hold several offers at once, that retention record is hard won and rare.
Beyond technical skill, we assess communication, stakeholder management and the ability to work across the business, because a data science hire who cannot translate their work into decisions, or who joins for the wrong reasons, is the one who leaves within a year. In data science and AI, where the strongest candidates have the most options, getting the fit right on both sides is the whole game. See how we work →
Your specialist
Led by a founder who knows this work first hand
Founder
Shagun Gupta
Founder, oversees all four core practices
EliteRecruitments was built by Shagun Gupta, who founded the firm out of actuarial and has recruited across actuarial, risk, analytics and audit, the four disciplines at the centre of the business. She is unusual among agency founders in that she stays close to the actual work rather than managing it from a distance, and analytics is one of her areas of genuine personal depth, alongside risk and audit. Analytics has been one of the firm’s practices since 2015, and she has overseen its growth from a supporting function into one of the firm’s strongest areas.
That depth matters most for fintech and insurtech firms, which increasingly hire blended roles, a pricing or risk role that is half data science or AI on the technical side and half actuarial or risk on the functional side. Because the firm covers analytics, AI, actuarial and risk together rather than as separate silos, it can fill briefs that fall between them, which a single discipline recruiter cannot.
Practice lead
Kanishka Gupta
Leads the analytics and AI practice, under Shagun
Kanishka Gupta leads our analytics and AI practice, under Shagun. She has built analytics and data science desks at EliteRecruitments since 2018, recruiting across analytics, data science, machine learning and the wider technology and finance roles that surround them. Her focus is on discerning genuine potential and matching people to the cultures and teams where they will do their best work, and she regularly advises clients on analytics and AI hiring strategy, capability centre team design and the shifting data science talent market.
She and her team work directly with hiring managers building data and analytics functions, so when you talk to us about a data science or AI role, you are talking to someone who understands the work and the market, not a generalist passing on a brief.
Proof
When we place, they tend to stay
Fewer than fifteen fees returned since 2014. Representative mandates, shown as role, sector and city, without names, counts or salaries:
Head of Analytics
Data Governance Lead
AI Product Manager
Machine Learning Lead
Illustrative of the role types we recruit. Specific anonymised placements to be confirmed before publishing.
They understood exactly the kind of analytics talent we needed and the level we were hiring at. They were quick, precise and genuinely understood the domain, which made the whole process easier.
Success story
Challenge. A global insurer was standing up a data science and analytics function in its India capability centre and needed experienced people who could both build models and lead, the scarcest band in the market.
Approach. We mapped the talent, screened hard for applied capability rather than CV polish, and matched on long term goals.
Outcome. The core team was hired, and the early senior hires are still leading the function more than two years on.
Illustrative format. To be replaced with a real anonymised analytics placement before publishing.
Analytics talent for a new centre of excellence, built at speed during a period when few people were moving, and closed in about four weeks.
Insurance grade machine learning talent, several hires placed at once, all with genuine insurance domain backgrounds.
More examples are on our Success Stories page.
Related intelligence
Insights from the analytics and AI practice
GCC
The 2026 GCC hiring boom
Why India’s capability centres are the centre of data science and AI hiring, and which roles are hardest to fill.
Read the articleAI
How AI governance is reshaping data science hiring
Why model risk, AI governance and compliance roles have gone from rare to essential in regulated firms.
Read the articleTalent
The missing middle in analytics and AI
Why the experienced build and lead cohort is the scarcest band, and how to hire for it.
Read the articleSourcing
How we find passive data science and AI talent that local agencies miss
Why the strongest professionals are rarely applying, and how specialist sourcing reaches them.
Read the articleHow to work with us
Capability build first, with a 90 day guarantee
We recruit analytics and AI talent on the basis that suits the role. Capability build recruitment comes first for a reason: when you are standing up an analytics, data science or AI function from scratch, which is exactly what many capability centres are doing now, we help you hire the whole team rather than one role at a time. Alongside it we offer retained search for senior and business critical hires, contingency for active roles, and recruitment process outsourcing where you are hiring at volume. For leadership roles, our executive search process applies.
Whichever way we work, our placements come with a 90 day guarantee. If a placement does not work out within 90 days, we replace them. Since 2014 we have had to return fewer than fifteen fees, because our process is built so the guarantee rarely needs to be used. Explore Capability Build Recruitment →
One connected ecosystem
Analytics and AI rarely sits on its own
The same firms building data science teams are also hiring the actuaries whose models depend on that data, the risk and model validation specialists who govern it, and the audit professionals who test it. These functions are one connected ecosystem, and we recruit across all of them. An analytics and AI relationship with us often grows into actuarial, risk and audit, just as our actuarial relationships often grow into data science.
Common questions
Frequently asked questions
What analytics and AI roles does EliteRecruitments recruit for?
Does EliteRecruitments recruit data science and AI talent for GCCs in India?
Can you hire a whole analytics or AI team, not just individuals?
How does EliteRecruitments assess data science and AI candidates?
Do you recruit AI governance and model risk roles?
Do you recruit Gen AI and Agentic AI specialists?
Do you recruit data product owners and analytics programme managers?
Do candidates pay any fees?
Start a conversation
Talk to us about analytics and AI hiring
Whether you are building an analytics or AI team in a capability centre, hiring a single hard to find data scientist or AI specialist, or weighing your own next move, we would like to help.
Thank you, we will be in touch shortly.
Your note goes to Kanishka and Shagun directly.