Tata Consultancy Services
Website:
tcs.com
Job details:
Exp Range- 5 to 10
Hiring Location- Kolkata, Hyderabad, Bhubaneshwar, Pune, Kochi, Ahmedabad and Chennai
Role: Data Science Platform – SME / Technical Lead
About the Role
Hands-on Designer to design, build, and scale enterprise Data Science platforms powered by modern data and AI ecosystems. This role focuses on enabling self-service analytics and intelligent decision-making through robust architecture, leveraging platforms such as Dataiku.
You will play a key role in bridging data engineering, data science, and business domains to deliver scalable, governed, and AI-powered insight solutions.
Key Responsibilities
- Architect and design end-to-end Data Science platforms leveraging Dataiku (on EKS) ensuring scalability, modularity, and performance.
- Design and implement data-to-insight pipelines including ingestion, transformation, feature engineering, and consumption layers across structured and unstructured data.
- Enable and scale self-service analytics and data science workflows, empowering users through reusable components, governed datasets, and standardized templates.
- Lead the design of collaborative analytics ecosystems, integrating, Dataiku for workflow orchestration and ML lifecycle
- Define and implement ML enablement patterns, including:
- Experimentation frameworks
- Model operationalization (MLOps)
- Architect and optimize cloud-native solutions on AWS, including EKS, S3, ensuring security, cost optimization, and reliability.
- Establish data modelling and semantic layer design standards to ensure consistent, business-aligned insight delivery.
- Design and promote best practices for insight consumption, including dashboards, APIs, notebooks, and GenAI-driven interfaces.
- Build reusable accelerators, frameworks, and templates to industrialize analytics and AI delivery.
- Collaborate with business, product, and engineering teams to translate business problems into scalable ML solutions.
- Drive adoption and enablement, mentoring teams on platform usage, design principles, and best practices.
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Data Engineering, Analytics, or related field
- Strong experience in data/analytics architecture, insight platforms, or AI enablement roles
- Hands-on expertise with Dataiku (including deployment on EKS/Kubernetes)
- Strong understanding of modern data architectures (Datamesh, lakehouse, data products, semantic layers)
- Proficiency in Python analytics and ML workflows
- Experience designing and implementing data pipelines and ML workflows on AWS
- Familiarity with MLOps practices (CI/CD, model monitoring, reproducibility)
- Strong stakeholder engagement and ability to translate business needs into technical solutions
Nice to have Qualifications
- Experience integrating GenAI capabilities into insight platforms (e.g., automated insights, conversational analytics, RAG use cases)
- Knowledge of Kubernetes (EKS) and containerized workloads for analytics platforms
- Experience with Snowflake or similar cloud data platforms
- Familiarity with DataOps and platform engineering practices
- Exposure to regulated domains (e.g., Insurance, Finance, Healthcare)
Key Traits
- Strong design and architecture mindset with focus on scalability and usability
- Passion for self-service enablement and democratizing data & AI
- Ability to operate across strategy, design, and hands-on implementation
- Excellent collaboration and influence across cross-functional teams
Click on Apply to know more.