Role: Senior Data Scientist
Required Skills: Data scientist
Qualification: BACHELOR OF COMPUTER SCIENCE
Experience: 10 - 16 Years
Function: TECHNOLOGY
Apply By: 2026-12-08
Location: NJ/NYC/Charlotte, NC/Columbus OH/Washington DC area/Malvern, PA/Boston
Must Have Technical/Functional Skills
- 10+ years in data science, advanced analytics, or applied statistics, including 3+ 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 least 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 engineer ing 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 options 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 Employee Benefits Summary:
Discretionary Annual Incentive.
Comprehensive Medical Coverage: Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans.
Family Support: Maternal & Parental Leaves.
Insurance Options: Auto & Home Insurance, Identity Theft Protection.
Convenience & Professional Growth: Commuter Benefits & Certification & Training Reimbursement.
Time Off: Vacation, Time Off, Sick Leave & Holidays.
Legal & Financial Assistance: Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing.
Salary Range: $120,000 - $150,000 a year