About Exponentia.ai :

Exponentia.ai is a fast-growing AI-first technology services company, partnering with enterprises to shape and accelerate their journey to AI maturity. With a presence across the US, UK, UAE, India, and Singapore, we bring together deep domain knowledge, cloud-scale engineering, and cutting-edge artificial intelligence to help our clients transform into agile, insight-driven organizations.

We are proud partners with global technology leaders such as Databricks, Microsoft, AWS, and Qlik, and have been consistently recognized for innovation, delivery excellence, and trusted advisories.

Awards & Recognitions :

- Innovation Partner of the Year - Databricks, 2024

- Digital Impact Award, UK - 2024 (TMT Sector)

- Rising Star - APJ Databricks Partner Awards 2023

- Qlik's Most Enabled Partner - APAC

About the Role :

We are looking for a Data Scientist to develop high-quality propensity models and customer analytics solutions using statistical analysis, machine learning, feature engineering, and experimentation.

The candidate will work closely with business stakeholders, analytics teams, and ML Engineers to translate customer data into predictive insights and measurable business actions. The role also involves supporting model deployment, monitoring, governance, and post-deployment performance reviews on AWS.

Key Responsibilities :

1. Business Understanding & Analytical Planning :

- Understand business use cases, target definitions, decision points, and measurable success criteria.

- Collaborate with business and analytics stakeholders to translate requirements into analytical solutions.

- Define appropriate analytical approaches for propensity, response, retention, and customer outcome models.

2. Data Analysis & Feature Engineering :

- Perform exploratory data analysis, data-quality assessment, cohort analysis, and leakage checks.

- Develop meaningful features from large, structured customer datasets.

- Identify patterns, trends, anomalies, and factors influencing customer behavior.

- Validate data suitability and feature stability across customer segments and time periods.

3. Model Development & Validation :

- Develop, compare, tune, and validate predictive models for propensity, response, retention, and related customer outcomes.

- Apply algorithms such as logistic regression, decision trees, random forest, gradient boosting, XGBoost, and LightGBM.

- Use appropriate sampling, feature-selection, cross-validation, and hyperparameter-tuning techniques.

- Evaluate models using ROC-AUC, PR-AUC, precision, recall, F1-score, lift, gains, calibration, confusion matrix, and stability assessment.

- Assess ranking quality, business lift, calibration, and performance for imbalanced classification problems.

4. AWS Deployment & Model Monitoring :

- Partner with ML Engineers to package models and feature logic for repeatable deployment on AWS.

- Work with AWS services such as S3, Athena, Glue, and SageMaker.

- Support user acceptance testing, output validation, monitoring thresholds, and post-deployment performance reviews.

- Contribute to secure, scalable, maintainable, and cost-aware implementation of analytical solutions.

5. Documentation & Stakeholder Communication :

- Document model assumptions, methodology, features, experiments, results, limitations, and recommended actions.

- Present analytical findings through concise narratives, visualizations, and business recommendations.

- Communicate effectively with both technical and non-technical stakeholders.

- Translate model results into practical and measurable business actions.

Ideal Candidate Profile :

- 3 - 5 years of experience in Data Science, Machine Learning, or Predictive Analytics.

- Strong proficiency in Python, SQL, Pandas, NumPy, and Scikit-learn.

- Experience building Propensity, Churn, Retention, or Customer Analytics Models.

- Good understanding of Statistics, Feature Engineering, Model Validation, and ML Algorithms.

- Working knowledge of AWS services (S3, Athena, Glue, SageMaker).

- Strong analytical, problem-solving, and stakeholder communication skills.

Preferred Qualifications :

- Bachelor's or Master's degree in Statistics, Mathematics, Economics, Computer Science, Data Science, Engineering, or a related quantitative discipline.

- Experience in BFSI, insurance, telecom, retail, or another customer-intensive industry.

- Exposure to explainable AI techniques, including SHAP.

- Familiarity with model monitoring, governance, and performance-drift concepts.

- Ability to translate analytical findings into measurable business actions.

Why Join Exponentia.ai?

- Innovate with Purpose : Opportunity to create pioneering AI solutions in partnership with leading cloud and data platforms.

- Shape the Practice : Build a marquee capability from the ground up with full ownership.

- Work with the Best : Collaborate with top-tier talent and learn from industry leaders in AI.

- Global Exposure : Be part of a high-growth firm operating across US, UK, UAE, India, and Singapore.

- Continuous Growth : Access to certifications, tech events, and partner-led innovation labs.

- Inclusive Culture : A supportive and diverse workplace that values learning, initiative, and ownership.