Senior Data Scientists / Analytics Leads

Tata Consultancy Services; Columbus (OH); On-site; USD 120,000 - 150,000; Full time

Benefits offered by this job

Discretionary incentive

Comprehensive medical coverage

Parental leaves

Insurance options

Certification & training reimbursement

Time off & holidays

Legal & financial assistance

401K plan

Job summary

Tata Consultancy Services Limited is seeking a senior data science leader in the Columbus area to drive data-driven decisions for BFSI use cases. You will lead hands-on exploration, build models, validate rigorously, and hand off production-ready solutions.

Expect collaboration across onshore teams, governance, and communication of results with business leaders. Strong Python/SQL, ML lifecycle, and GenAI use will be valuable.

Qualifications

• 10+ years in data science, analytics, or statistics with 3+ years leading analytical workstreams.
• Built and validated predictive models; not BI-only leadership.
• Strong Python/SQL with data science libraries; PySpark for large datasets.
• Experience with BFSI use cases and appropriate validation metrics.
• Experience with A/B testing or quasi-experimental methods.
• Explainability and governance: SHAP, bias checks, validation documentation.
• Production mindset: Git, code reviews, reproducible pipelines, MLflow tracking, monitoring.
• Clear presentation of results with KPI definitions and actionable score cut-offs.
• GenAI usage for classification/summarization of unstructured data; familiarity with LLM tooling.

Responsibilities

• Turn client questions into an analysis plan with targets, data sources, baselines, success metrics, and milestones.
• Lead hands-on data exploration and preparation in SQL, Python, or PySpark.
• Build and review models for BFSI use cases with appropriate validation and business-cost aligned metrics.
• Measure business impact via A/B tests or quasi-experimental methods; report uplift with confidence intervals.
• Deliver recommendations as presentations, dashboards, or score cut-offs and rules.
• Lead a small team of data scientists and analysts, coordinating onshore/offshore contributors.
• Handoff production‑ready models to ML engineers with scoring specs and monitoring thresholds.
• Track model performance post-launch, recalibrate as needed; document development and validation.

Education

Bachelor of Computer Science

Tools

Python

SQL

pandas

NumPy

scikit-learn

XGBoost/LightGBM

PySpark

Databricks

SageMaker

Vertex AI

Git

MLflow

Power BI

Tableau

SHAP

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

Desired Candidate Profile

Qualifications

BACHELOR OF COMPUTER SCIENCE