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Tier-1 brand, mid-level generalist ML role, and metro location increase applicant competition.
Core ML, MLOps, and cloud skills are broadly transferable across industries.
Explicit 4+ years plus mandatory ML, LLM, cloud and deployment skills raise filtering rigidity.
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Own end-to-end data science and machine learning solutions including problem formulation, model development, deployment, and monitoring in production.
Design scalable data and ML systems across cloud platforms and ensure business impact through collaboration with product, engineering, and business stakeholders.
Lead and mentor junior team members by supervising tasks, managing projects for internal and external clients, and setting best practices in data science and ML.
4+ years of relevant experience in data science or machine learning roles.
Proficiency in Python and advanced SQL, with hands-on experience in ML frameworks such as XGBoost, LightGBM, PyTorch, or TensorFlow.
Expertise in at least one major cloud platform: AWS, Azure, or GCP, including managed ML and data services.
Experience with data engineering tools (Spark/PySpark) and ML lifecycle management (MLOps) including CI/CD, containerization (Docker/Kubernetes) is strongly preferred.
Senior-level data scientist who can independently manage full ML lifecycle including deployment and monitoring in production.
Technical leader experienced in building scalable ML architectures and collaborating cross-functionally with product and engineering teams.
Experienced mentor capable of guiding junior data scientists, enforcing ML best practices, and managing complex data and ML projects.