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\/p> Data Scientist - Financial Modeling & AWS - Location: India (Gurgaon) / Bangalore - Two days per month WFO - Employment Type: 6 month Contract - Budget: INR 250k per month - Primary Focus : Financial forecasting & risk modelling; AWS -based model development - Immediate Joiners only - Yrs. of Exp: 6 + - Location - Permanent Remote with Mandatory 2 Days in a month from Gurgaon / Bengaluru office Role Summary We are seeking a hands -on Data Scientist with expertise in financial modelling and AWS -based solutions. You will design, develop, and deploy advanced statistical and machine learning models that turn complex financial data into actionable insights and scalable, production -ready solutions. Key Responsibilities - Build predictive and prescriptive models for financial forecasting, risk analysis, and decision optimization. - Apply statistical and machine learning techniques to improve business outcomes and model performance. - Perform data wrangling, cleansing, and feature engineering on large structured and unstructured datasets. - Develop and maintain robust ETL pipelines for financial data; ensure reproducibility and data lineage. - Use AWS services (e.g., SageMaker, Glue, Redshift, Lambda) for model development, training, and deployment. - Design solutions that are secure, scalable, and cost -efficient on AWS. - Partner with cross -functional stakeholders to align modeling approaches with business objectives. - Present insights, model outcomes, and recommendations to technical and non -technical audiences clearly. Required Skills & Qualifications - Strong programming skills in Python, PySpark, and SQL. - Hands -on experience with AWS services for data science workflows (e.g., SageMaker, Glue, Redshift, Lambda). - Solid understanding of financial principles and quantitative analysis. - Proven ability to deliver end -to -end data science projects in production environments. - Solid problem -solving skills and ability to translate ambiguous requirements into measurable outcomes. Preferred Qualifications - Familiarity with big data technologies (Hadoop, Spark). - Knowledge of data visualization / BI tools (e.g., Tableau, Power BI). - Experience with MLOps and model monitoring in production environments. .