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Mid-level Gurgaon ML role with common title and in-demand cloud/ML skills yields moderate competition.
Applied ML and cloud tooling are transferable, though financial domain experience is beneficial.
Explicit 5–7 years plus mandatory ML production, Python, AWS SageMaker, and Snowflake skills enforce strict filters.
Manage data intake processes, perform A/B testing and sales funnel analysis to generate actionable insights.
Build, validate, deploy, and monitor machine learning models end-to-end using Python and AWS SageMaker.
Partner with senior stakeholders to translate business problems into data science solutions and present findings effectively.
5-7 years of hands-on experience in data science or applied machine learning.
Proficiency in Python (pandas, numpy, scikit-learn) and SQL.
Experience with AWS SageMaker, Redshift and/or Snowflake, and S3 for large-scale data handling and model deployment.
Work Experience Required: 5-7 years in relevant fields. Notice period: Not explicitly mentioned in the JD.
Strong data scientist capable of rigorous exploratory data analysis and statistical/machine learning modeling.
Proactive in challenging assumptions and ensuring models solve real business problems rather than technical curiosities.
Comfortable managing full-data lifecycle ownership from raw data analysis through to business impact and communication with technical and non-technical stakeholders.