Tata Consultancy Services
Overview
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Data Scientist
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Tata Consultancy Services Must Have Technical/Functional Skills Responsibilities
Druid Data Modeling & Schema Design: Design and implement efficient data schemas, dimensions, and metrics within Apache Druid for various analytical use cases (e.g., clickstream, IoT, application monitoring). Determine optimal partitioning, indexing (bitmap indexes), and rollup strategies to ensure sub-second query performance and efficient storage. Data Ingestion Pipeline Development: Develop and manage real-time data ingestion pipelines into Druid from streaming sources like Apache Kafka, Amazon Kinesis, or other message queues. Implement batch data ingestion processes from data lakes (e.g., HDFS, Amazon S3, Azure Blob, Google Cloud Storage) or other databases. Ensure data quality, consistency, and exactly-once processing during ingestion. Query Optimization & Performance Tuning: Write and optimize complex SQL queries (Druid SQL) for high-performance analytical workloads, including aggregations, filters, and time-series analysis. Analyze query plans and identify performance bottlenecks, implementing solutions such as segment optimization, query rewriting, or cluster configuration adjustments. Programming & Libraries: Expert-level proficiency in Python and its core data science libraries (Pandas, NumPy, Scikit-learn). Strong proficiency in SQL for complex data extraction and manipulation. Machine Learning Frameworks: Hands-on experience with modern deep learning frameworks such as TensorFlow or PyTorch. Statistical Modeling: Deep understanding of statistical concepts and a wide range of machine learning algorithms, with proven experience in time-series forecasting and anomaly detection. Big Data Technologies: Demonstrable experience working with large datasets using distributed computing frameworks, specifically Apache Spark. Database Systems: Experience querying and working with data from multiple relational database systems (e.g., PostgreSQL, Oracle, MS SQL Server). Cloud Platforms: Experience building and deploying data science solutions on a major cloud platform (AWS, GCP, or Azure). Familiarity with their native ML services (e.g., AWS SageMaker, Google Vertex AI) is a strong plus. MLOps Tooling: Practical experience with MLOps principles and tools for model versioning, tracking, and deployment (e.g., MLflow, Docker). Communication and Storytelling: Excellent verbal and written communication skills, with a proven ability to explain complex technical concepts to a non-technical audience through visual storytelling. Qualifications
Must Have Technical/Functional Skills: Programming & Libraries: Expert-level proficiency in Python and its core data science libraries (Pandas, NumPy, Scikit-learn). Strong proficiency in SQL for complex data extraction and manipulation. Machine Learning Frameworks: Hands-on experience with modern deep learning frameworks such as TensorFlow or PyTorch. Statistical Modeling: Deep understanding of statistical concepts and a wide range of machine learning algorithms, with proven experience in time-series forecasting and anomaly detection. Big Data Technologies: Demonstrable experience working with large datasets using distributed computing frameworks, specifically Apache Spark. Database Systems: Experience querying and working with data from multiple relational database systems (e.g., PostgreSQL, Oracle, MS SQL Server). Cloud Platforms: Experience building and deploying data science solutions on a major cloud platform (AWS, GCP, or Azure). Familiarity with their native ML services (e.g., AWS SageMaker, Google Vertex AI) is a strong plus. MLOps Tooling: Practical experience with MLOps principles and tools for model versioning, tracking, and deployment (e.g., MLflow, Docker). Communication and Storytelling: Excellent verbal and written communication skills, with a proven ability to explain complex technical concepts to a non-technical audience through visual storytelling. Salary
Salary Range: $100,000-$120,000 a year
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Join to apply for the
Data Scientist
role at
Tata Consultancy Services Must Have Technical/Functional Skills Responsibilities
Druid Data Modeling & Schema Design: Design and implement efficient data schemas, dimensions, and metrics within Apache Druid for various analytical use cases (e.g., clickstream, IoT, application monitoring). Determine optimal partitioning, indexing (bitmap indexes), and rollup strategies to ensure sub-second query performance and efficient storage. Data Ingestion Pipeline Development: Develop and manage real-time data ingestion pipelines into Druid from streaming sources like Apache Kafka, Amazon Kinesis, or other message queues. Implement batch data ingestion processes from data lakes (e.g., HDFS, Amazon S3, Azure Blob, Google Cloud Storage) or other databases. Ensure data quality, consistency, and exactly-once processing during ingestion. Query Optimization & Performance Tuning: Write and optimize complex SQL queries (Druid SQL) for high-performance analytical workloads, including aggregations, filters, and time-series analysis. Analyze query plans and identify performance bottlenecks, implementing solutions such as segment optimization, query rewriting, or cluster configuration adjustments. Programming & Libraries: Expert-level proficiency in Python and its core data science libraries (Pandas, NumPy, Scikit-learn). Strong proficiency in SQL for complex data extraction and manipulation. Machine Learning Frameworks: Hands-on experience with modern deep learning frameworks such as TensorFlow or PyTorch. Statistical Modeling: Deep understanding of statistical concepts and a wide range of machine learning algorithms, with proven experience in time-series forecasting and anomaly detection. Big Data Technologies: Demonstrable experience working with large datasets using distributed computing frameworks, specifically Apache Spark. Database Systems: Experience querying and working with data from multiple relational database systems (e.g., PostgreSQL, Oracle, MS SQL Server). Cloud Platforms: Experience building and deploying data science solutions on a major cloud platform (AWS, GCP, or Azure). Familiarity with their native ML services (e.g., AWS SageMaker, Google Vertex AI) is a strong plus. MLOps Tooling: Practical experience with MLOps principles and tools for model versioning, tracking, and deployment (e.g., MLflow, Docker). Communication and Storytelling: Excellent verbal and written communication skills, with a proven ability to explain complex technical concepts to a non-technical audience through visual storytelling. Qualifications
Must Have Technical/Functional Skills: Programming & Libraries: Expert-level proficiency in Python and its core data science libraries (Pandas, NumPy, Scikit-learn). Strong proficiency in SQL for complex data extraction and manipulation. Machine Learning Frameworks: Hands-on experience with modern deep learning frameworks such as TensorFlow or PyTorch. Statistical Modeling: Deep understanding of statistical concepts and a wide range of machine learning algorithms, with proven experience in time-series forecasting and anomaly detection. Big Data Technologies: Demonstrable experience working with large datasets using distributed computing frameworks, specifically Apache Spark. Database Systems: Experience querying and working with data from multiple relational database systems (e.g., PostgreSQL, Oracle, MS SQL Server). Cloud Platforms: Experience building and deploying data science solutions on a major cloud platform (AWS, GCP, or Azure). Familiarity with their native ML services (e.g., AWS SageMaker, Google Vertex AI) is a strong plus. MLOps Tooling: Practical experience with MLOps principles and tools for model versioning, tracking, and deployment (e.g., MLflow, Docker). Communication and Storytelling: Excellent verbal and written communication skills, with a proven ability to explain complex technical concepts to a non-technical audience through visual storytelling. Salary
Salary Range: $100,000-$120,000 a year
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