4–5 yrsPuneOn-siteDirect career pageEst. 14–28 LPA
Posted 11h ago
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Job description
Structured overview of role & requirements
About This Role
- Own the end-to-end lifecycle of production machine-learning models, from business problem definition and feature engineering through deployment, monitoring, retraining, and optimization.
- Build customer and portfolio solutions for churn, cross-sell, segmentation, recommendations, customer value, and portfolio optimization.
- Operationalize predictive and GenAI/LLM/NLP solutions with business, product, data, and MLOps teams; communicate outcomes and quantify business impact.
Minimum Requirements
- Work Experience Required: 4–5 years in Data Science, Machine Learning, Advanced Analytics, or a related field.
- Strong proficiency in Python and SQL, with proven experience developing, deploying, and maintaining production-grade machine-learning models.
- Experience in customer, portfolio, commercial, CRM, marketing, or customer-lifecycle analytics, including predictive analytics use cases.
- Experience with data science platforms such as Databricks, Snowflake, AWS SageMaker, or similar cloud environments; familiarity with MLOps practices such as version control, monitoring, and deployment.
Ideal Candidate Profile
- Has delivered predictive models using large-scale customer, transaction, sales, or operational data and can connect model outputs to business outcomes.
- Can translate portfolio and customer-growth challenges into scalable analytics, data pipelines, decision-support tools, and adopted solutions across global stakeholders.
- Offers exposure to GenAI, LLMs, NLP, conversational intelligence, or experimentation; Financial Services, Payments, FinTech, or Banking experience is preferred.