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High applicant competition due to Tier-1 employer, mid-level data science role, metro location, and broad skill requirements.
Medium because core ML skills transfer across industries but banking domain knowledge and governance add constraints.
High due to explicit 5+ years requirement and mandatory ML, programming, and big-data/cloud skills.
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Design, develop, and deploy advanced machine learning models and predictive algorithms addressing complex business problems, ensuring scalability and production-readiness.
Conduct exploratory data analyses to extract actionable insights and communicate findings to both technical and non-technical stakeholders.
Collaborate with business units to frame problems, translate requirements into technical specifications, and guide junior data scientists through mentorship and best practice development.
Minimum 5+ years of professional experience in data science, machine learning, or related analytical roles.
Proficiency in Python (with libraries such as scikit-learn, TensorFlow, PyTorch) or R, and experience with SQL and large-scale databases.
Strong background in statistical modeling, hypothesis testing, experimental design, and machine learning algorithms including deep learning and NLP.
Work Experience Required: 5+ years; Notice Period: Not explicitly mentioned in the JD.
Experienced in end-to-end machine learning workflow including model development, deployment, and monitoring preferably with MLOps exposure.
Comfortable working cross-functionally with product managers, engineers, and data engineering teams within a fast-paced corporate environment.
Capable of translating complex analytical concepts to diverse stakeholders and mentoring junior staff, indicating readiness for leadership responsibilities.