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.
Strong 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.