Scientist, Data Science

XPO; Maharashtra; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

XPO is seeking a data-driven Data Scientist in Maharashtra to join our growing Data Science team. You will bridge the gap between complex data and strategic business decisions, collaborating with product, engineering, and business teams to design, build, and deploy predictive models and data products that drive measurable impact.

You will apply statistical analysis, ML algorithms, and software development practices to solve open-ended business problems and deliver actionable insights for

Qualifications

• 3+ years of professional data science experience.
• Bachelor's or master's in a quantitative field.
• Proficiency in Python with libraries like pandas, numpy, scikit-learn.
• Advanced SQL skills for data extraction and manipulation.
• Strong understanding of ML concepts and algorithms.
• Experience with cloud environments and version control.

Responsibilities

• Model Development: design, build, evaluate, and iterate on ML models to solve business challenges.
• Data Pipelines & ETL: clean, preprocess, and feature engineer large-scale datasets.
• Insights & Analytics: perform EDA and apply statistical methods to uncover trends.
• Collaboration & Communication: translate findings into actionable insights for stakeholders.
• Production Deployment: work with Data Engineers and MLOps to deploy models.
• Team Contribution: participate in code reviews and document best practices.

Skills

Data analysis

Problem solving

Team collaboration

Communication

Education

Bachelor's or Master's in a quantitative field

Tools

Python

SQL

Git

Cloud platforms (AWS/GCP/Azure)

Job description

We are seeking a data-driven and analytical
Data Scientist with minimum 3 years of professional experience to join our growing Data Science team. In this role, you will bridge the gap between complex data and strategic business decisions. You will collaborate closely with product, engineering, and business teams to design, build, and deploy predictive models, extract actionable insights, and build data products that drive measurable impact.

The ideal candidate has a strong foundation in statistical analysis, machine learning algorithms, and software development practices, combined with a passion for solving open-ended business problems.

Key Responsibilities

• 
  Model Development: Design, build, evaluate, and iterate on machine learning models (supervised and unsupervised) to solve key business challenges such as [e.g., churn prediction, recommendation engines, fraud detection, demand forecasting].
• 
  Data Pipelines & ETL: Clean, preprocess, and feature-engineer large-scale structured and unstructured datasets from disparate sources to ensure high-quality inputs for modeling.
• 
  Insights & Analytics: Perform exploratory data analysis (EDA) and apply advanced statistical methods (A/B testing, regression analysis, hypothesis testing) to uncover trends and patterns.
• 
  Collaboration & Communication: Translate complex analytical findings into clear, actionable insights and visualizations for both technical and non-technical stakeholders.
• 
  Production Deployment: Partner with Data Engineers and MLOps to deploy models into production environments and monitor their performance over time.
• 
  Team Contribution: Actively participate in code reviews, share best practices, and contribute to the team's internal data science libraries and documentation.
  

Required Qualifications & Skills

• 
  Experience: Minimum of 3 years of professional, hands-on experience working as a Data Scientist or in a highly quantitative analytical role.
• 
  Education: Bachelor's or master's degree in computer science, Data Science, Statistics, Applied Mathematics, Economics, or a related quantitative field.
• 
  Programming: Strong proficiency in
  Python (preferred), including libraries like Pandas, NumPy, Scikit-Learn, and SciPy.
• 
  SQL: Advanced proficiency in writing clean, optimized SQL queries to extract and manipulate data from relational databases.
• 
  Machine Learning: Solid understanding of core machine learning concepts (e.g., linear/logistic regression, decision trees, random forests, gradient boosting, clustering, evaluation metrics).
• 
  Cloud & Tools: Experience working within cloud environments (AWS, Google Cloud, or Azure) and version control tools (
  Git).