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About This Role
Must Have Technical/Functional Skills
- Bachelor’s degree in Data Science, Statistics, Computer Science, Economics, Engineering, or related field; advanced degree preferred.
- 7\+ years of applied data science experience, with at least 5 years in Talent/People Analytics, or consulting for large enterprises.
- Demonstrated experience delivering end\-to\-end analytics and deploying models to production in cross\-functional environments.
- Strong experience with HR systems and data models (Workday, PeopleSoft) or equivalent enterprise HR data experience.
- Modeling \& methods: strong foundations in statistical modeling (linear/logistic regression, survival analysis/time\-to\-event where relevant), tree\-based methods, clustering, causal methods, and applied NLP/transformer/LLM techniques for text\-based HR applications.
- Programming: production\-capable Python coding (modular design, testing, packaging), experience with version control (Git), and collaboration with DevOps/CI\-CD workflows.
- Data engineering \& infrastructure: experience working with ETL, feature engineering, data warehouses/lakes, and modern cloud platforms; familiarity with Spark, dbt, Airflow, or equivalents desirable.
- Model lifecycle \& tooling: familiarity with model registries and lifecycle tools (MLflow, Seldon, Terraform/Helm or equivalent), explainability tools (SHAP, LIME), fairness/tooling (AIF360 or equivalent), and monitoring frameworks.
- Querying \& visualization: advanced SQL skills; experience with BI/visualization tools (Tableau, Power BI) and producing executive\-ready dashboards and narratives.
- Privacy \& security: practical knowledge of de\-identification, synthetic data, and access\-control patterns for sensitive HR data.
Roles \& Responsibilities
- Lead end\-to\-end analytic projects: define problem statements with HR stakeholders, design experiments, select appropriate methods, develop models, validate results, and deliver production\-ready solutions and monitoring.
- Build predictive and prescriptive models for talent use cases (attrition/retention, internal mobility, promotion forecasting, performance indicators, recruitment sourcing/scoring, skilling/curation, compensation analytics).
- Develop and productionize features and models in collaboration with data engineers and ML engineers: implement reproducible ETL, feature pipelines, model training pipelines, CI/CD, and deployment patterns.
- Apply statistical methods, hypothesis testing, causal inference where appropriate, and robust validation (cross\-validation, holdouts, calibration, fairness testing) to ensure reliable, defensible results.
- Design and operationalize NLP/LLM solutions for HR use cases (resume parsing, candidate experience, employee feedback analysis) while enforcing privacy, data minimization and explainability requirements.
- Instrument model monitoring and drift detection; define alerting, retraining triggers, and remediati on plans.
- Produce clear, actionable visualizations and dashboards that tell the story of analytic findings and drive decisions; collaborate with BI developers to operationalize reporting.
- Translate technical analyses into business recommendations, quantify expected impact, and work with partners to implement changes and measure outcomes.
- Mentor junior data scientists/analysts, review code and model artifacts, and help raise team standards for reproducibility, documentation, and governance.
- Ensure models and data products adhere to governance, privacy, and ethical requirements; collaborate with HR Data Steward, Legal/Privacy, and Ethics/AI governance on reviews and approvals.
Generic Managerial Skills, If any
- Problem\-solver with product mindset: frames analytics as business products with clear KPIs and adoption plans.
- Ownership \& results orientation: takes accountability for delivery, end\-to\-end operation, and measurable impact.
- Communication \& storytelling: synthesizes complex analyses into concise recommendations for HR leaders and executives.
- Collaboration \& influence: builds strong cross\-functional relationships and navigates competing priorities.
- Coaching \& development: mentors peers and contributes to team capability growth.
- Ethical judgment: prioritizes fairness, privacy, and employee impact in modelling decisions.
Base Salary Range : $110,000 to $140,000 Per Annum
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.
\#LI\-SV2
\#LI\-KUMARAN
Location
Houston, TX
Job Function
TECHNOLOGY
Role
Data Scientist
Job Id
416047
Desired Skills
Data scientist
Salary Range
$110,000\-$140,000 a year
Desired Candidate Profile
Qualifications : BACHELOR OF COMPUTER SCIENCE
Salary Context
This $110K-$140K range is in the lower quartile for Data Scientist roles in our dataset (median: $157K across 236 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Tata Consultancy Services (TCS), this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 808 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($125K) sits 37% below the category median. Disclosed range: $110K to $140K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Tata Consultancy Services (TCS) AI Hiring
Tata Consultancy Services (TCS) has 27 open AI roles right now. They're hiring across AI/ML Engineer, AI Consultant, Data Scientist, AI Architect. Positions span Sunrise, FL, US, Atlanta, GA, US, Austin, TX, US. Compensation range: $90K - $210K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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