Senior Data Scientists / Analytics Leads

Role: Analyst. Skills: Data Analytics. Experience: 5 - 15 Years

Inclusion without Exception

Tata Consultancy Services (TCS) is an equal opportunity employer, and

embraces diversity in race, nationality, ethnicity, gender, age, physical

ability, neurodiversity, and sexual orientation, to create a workforce that

reflects the societies we operate in. Our continued commitment to Culture and

Diversity is reflected in our people stories across our workforce and implemented

through equitable workplace policies and processes.

Tata Consultancy Services (BSE: 532540, NSE: TCS) is the

technology partner of choice for industry-leading organizations worldwide.

Since its inception in 1968, TCS has upheld the highest standards of

innovation, engineering excellence and customer service.

It has set an aspiration to become the world's largest

AI-led technology services company and is enabling its clients to transform

themselves across the full AI stack, from infrastructure to intelligence.

Rooted in the heritage of the Tata Group, TCS is focused on

creating long term value for its clients, its investors, its employees, and the

community at large. With a highly skilled workforce spread across 56 countries

and 194 service delivery centers across the world, the company has been

recognized as a top employer in six continents. With the ability to rapidly

apply and scale new technologies, the company has built long term partnerships

with its clients. Many of these relationships have endured into decades and

navigated every technology cycle, from mainframes in the 1970s to artificial

intelligence today.

Role Description

• years in data science, advanced analytics, or applied statistics, including years

leading analytical workstreams or mentoring data scientists. Must have

personally built and validated predictive models; BI-only leadership is not

sufficient.

• Strong Python and SQL, including pandas, NumPy, scikit-learn, and XGBoost or

LightGBM. PySpark for larger datasets and experience with environments such as

Databricks, SageMaker, or Vertex AI are useful, not required.

• Solid statistics: hypothesis testing, linear and logistic regression,

time-series methods, sampling, and clear communication of uncertainty.

• Model development for at le ast one BFSI use case such as credit risk,

fraud/AML alert prioritization, propensity/churn, collections, or forecasting.

Choose validation and metrics appropriate to the problem (e.g., out-of-time

tests, AUC/KS for scorecards, precision/recall for alerts, MAE for forecasts).

• Experience measuring business impact with A/B tests where feasible or with

defensible observational approaches such as matched cohorts,

difference-in-differences, or controlled pre/post comparisons; not every method

is required.

• Explainability and model governance: use SHAP or similar techniques, assess

bias where relevant, and document assumptions, limitations, and validation. For

US banking models, familiarity with SR 11-7 expectations is useful;

credit-decision reason codes apply only to relevant credit use cases.

• Production awareness: Git, code review, reproducible pipelines, unit and data

tests, MLflow tracking, and handoff to ML engineering for batch scoring or

APIs, with monitoring such as PSI and performance drift.

• Clear presentation of results: KPI definitions, visuals, and decision rules

or score cut-offs that business teams can act on. Power BI or Tableau is a

plus.

• Practical GenAI use: applying LLMs to classify, summarize, or extract data

from unstructured text (e.g., complaints, KYC documents) and evaluating output

quality. LLM infrastructure experience is not required.

• Client-facing leadership: frame ambiguous problems, agree on outcomes, review

code and results, and guide a small team; experience coordinating across time

zones is useful when delivery is distributed. BFSI experience strongly

preferred.

Roles & Responsibilities

• Turn a client business question into an analysis plan covering target

population, data sources, baseline, success metric, and milestones.

• Lead hands-on data exploration and preparation in SQL, Python, or PySpark;

resolve data issues with data engineering and document definitions and

exclusions.

• Build and review models for BFSI use cases, choosing validation schemes and

metrics that reflect business cost (e.g., fraud losses vs. analyst review

effort).

• Measure business impact through A/B tests or quasi-experimental methods,

reporting uplift with confidence intervals.

• Deliver recommendations as presentations, dashboards, or score cut-offs and

decision rules agreed with the business.

• Lead a small team of data scientists and analysts: break down work, review

code and results, manage delivery quality, and contribute hands-on; coordinate

onshore/offshore contributors where the engagement requires it.

• Hand off production-ready models to ML engineering with scoring

specifications, test datasets, and monitoring thresholds; agree ownership for

release and support.

• Track models after launch: PSI and drift, actual vs. expected performance,

and business adoption; recommend recalibration or retraining when needed.

• Prepare model-development documentation and support independent validation

where applicable, addressing findings with evidence such as validation results,

SHAP explanations, or fairness tests; include credit-decision reason codes when

relevant.

• Report progress, findings, and limitations to client sponsors, and help scope

and estimate follow-on work.

Generic Managerial Skills, If any

• Strong leadership and stakeholder management skills with the ability to work

effectively across business, data, engineering, and architecture teams.

• Client-facing ability, excellent communication and presentation skills for

technical and business.

• Strong consulting and problem-solving mindset with the ability to evaluate

option s and recommend practical solutions.

• High ownership, accountability, and attention to detail with a focus on

quality, governance, and customer success.

• Strong client-facing experience supporting workshops, architecture reviews,

solution presentations, and executive discussions.

TCS does not use artificial intelligence tools for candidate screening or

evaluation. This post is for a current vacancy. The hiring process includes an

initial screening, followed by a technical evaluation and managerial

discussion.

Tata Consultancy Services Canada Inc. is

committed to meeting the accessibility needs of all individuals in accordance

with the Accessibility for Ontarians with Disabilities Act (AODA) and the

Ontario Human Rights Code (OHRC). Should you require accommodation during the

recruitment and selection process, please inform Human Resources.

Thank you for your interest in

TCS. Candidates that meet the qualifications for this position will be

contacted within a 2-week period. We invite you to continue to apply for other

opportunities that match your profile.