Data Scientist

About the Role

We are looking for someone who delivers machine learning and modern AI (including generative AI where relevant) solutions end-to-end, from problem framing to production deployment, partnering closely with product, data engineering and business teams.

Location | Experience | How to Apply:

Location: Hyderabad

Experience: 5+ years

How to Apply: Interested candidates can also apply directly via the Infosys Careers Page - https://career.infosys.com/jobdesc?jobReferenceCode=INFSYS-EXTERNAL-253618&rc=0&jobType=normal

Global stakeholder exposure

Opportunity to be part of a large-scale transformation journey

Good to have experience in consumer goods, manufacturing, retail, or related industry domains.

Responsibilities

• Translate business needs into ML/AI problem statements and measurable success metrics.
• Develop ML models and, where relevant, GenAI components (e.g., retrieval-augmented generation, prompt pipelines) with clear evaluation criteria.
• Run evaluation: offline metrics, error analysis, bias checks, and monitoring baselines; document decisions and assumptions.
• Communicate results and limitations clearly to technical and non-technical stakeholders; support adoption in workflows.

Qualifications

• Experience: typically, 5+ years delivering end-to-end data science projects.
• Education: Masters or PhD in Data Science, Machine Learning, Statistics, Computer Science, Applied Mathematics, or related quantitative field (required from this level onward).
• Core stack: Python, Spark, Git; ML frameworks; Databricks/MLflow (or equivalent); cloud basics.

Tools / platforms (expected proficiency)

• Python (pandas, numpy) + Git for reproducible development
• Databricks (Notebooks, Workflows) for development and orchestration
• MLflow (experiments, tracking, model registry) for lifecycle management
• Azure (cloud services; where relevant Azure OpenAI and Azure AI Foundry for GenAI build/evaluation)
• Databricks Mosaic AI (including Mosaic AI Model Serving) for GenAI delivery in the lakehouse
• Databricks Vector Search for RAG retrieval patterns