Job description

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 </div

Requirements

Must-haves

Location: NJ/NYC/Charlotte, NC/Columbus OH/Washington DC area/Malvern, PA/Boston
Must Have Technical/Functional Skills
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.
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.
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.
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.
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.
Discretionary Annual Incentive.

Nice-to-haves

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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

pythonsqlaimlllm

Platform: careers-page
Effort: medium

Benefits

Benefits detected in the listing.

Dental

Vision

401k

Parental Leave