Senior Data Scientist

Must Have Technical/Functional Skill Hands-on experience on: 1. Programming Languages

• Strong Python familiarity (hands on) for data prep, modeling, and building ML components.
• SQL

• Skills:

  joins, window functions, CTEs, query optimization 2. Machine Learning
• Linear/Logistic Regression
• Decision Trees, Random Forest, XGBoost, LightGBM

SVM, KNN

• Model evaluation
• Precision/Recall, F1, ROC-AUC, MSE, RMSE
• Model tuning
• Grid search, randomized search, cross validation 3. Deep Learning

Frameworks:

TensorFlow, Keras, PyTorch

• CNNs, RNNs, LSTMs, Transformers
• Use cases: NLP, computer vision, time-series forecasting 4. Data Wrangling & Preprocessing
• Missing data handling
• Feature engineering
• Data cleaning
• Outlier detection
• Normalization/standardization 5. Data Visualization & BI Tools

Python:

Matplotlib, Seaborn, Plotly

Tools:

Tableau, Power BI

• Dashboards, reporting, storytelling with data 6. Big Data & Cloud Tools (Needed for production-scale roles)

Big Data Frameworks:

Spark, Hadoop

• Cloud Platforms (any one strongly): o AWS (S3, EC2, SageMaker) o Azure (Data Factory, Databricks, ML Studio) o GCP (BigQuery, Vertex AI) 7. Deployment Skills (advanced roles)
• Model deployment: Flask, FastAPI
• Docker, Kubernetes (optional)
• CI/CD basics 8. Databases & Data Engineering Basics

Relational:

MySQL, Postgre

SQL, SQL

Server

NoSQL:

MongoDB, Cassandra

• Data pipelines: Airflow, Prefect (optional) Roles & Responsibilities
• Define the ML use case, success metrics, and evaluation criteria; Liaise with business directly and translate business needs into an ML approach.
• Perform data exploration, data quality checks, feature engineering, and dataset preparation for training and testing.
• Build, train, validate, and iterate ML models; compare experiments and select the best candidate model.
• Package the solution f or production (e.g., containerized scoring/service endpoint) and support deployment with engineering/MLOps practices
• Set up basic monitoring (model accuracy/health) and support continuous improvement post release. Required Skills & Experience
• Solid foundation in ML concepts (supervised/unsupervised, evaluation, validation) and practical experimentation.
• Experience taking models to production in a cloud agnostic way (portable design; API/service mindset).
• Working knowledge of version control and basic CI/CD-style collaboration with engineering teams.

Salary Range:

$125,000

• 145,000 a year

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 & amp; 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-SP1