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Senior Data Scientist
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Tata Consultancy Services Limited
Pleasanton, CA (In Person)
$135,000 Salary, Full-Time
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Job Description
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, PlotlyTools:
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, PostgreSQL, SQL
ServerNoSQL:
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