• 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.
  

• ·       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.