About the Role :

We are looking for a high-impact Data Scientist / Machine Learning Engineer to build and deploy machine learning solutions that directly influence customer experience, personalization, and revenue growth.

In this role, you will work closely with Product, Engineering, and Data teams to identify high-impact ML opportunities, develop predictive models, and deploy production-grade AI systems at scale.

This is an exciting opportunity to work on real-world ML applications such as recommendation systems, personalization engines, pricing optimization, and fraud detection in a fast-paced product company environment.

Key Responsibilities :

Build and deploy production-grade ML models :

- Develop machine learning models to power personalization, recommendation systems, and user

ranking

- Build models to optimize pricing, discounts, and revenue growth

- Implement propensity models for conversion, churn, and user engagement

- Design fraud detection and risk scoring systems

Work with large-scale data :

- Process and analyze large structured and unstructured datasets

- Build robust feature engineering pipelines and data contracts

- Extract insights from text and behavioral datasets

Develop advanced ML solutions :

- Build models using classical ML algorithms and deep learning

- Implement NLP-based solutions including text classification, embeddings, and similarity models

- Build user profiling and recommendation engines

Productionize ML systems :

- Deploy models using APIs, Docker, CI/CD pipelines

- Collaborate with engineering teams to deploy solutions on AWS / GCP environments

- Build systems for model monitoring, data drift detection, and performance tracking

Experimentation and optimization :

- Design and run A/B experiments to validate ML impact

- Track business KPIs and model performance

- Continuously improve models based on product and user data

Ideal Candidate :

- We are looking for a strong Data Scientist / Machine Learning Engineer with hands-on experience building real-world ML systems.

Mandatory Requirements :

Experience :

- Minimum 2+ years of hands-on experience as a Data Scientist or Machine Learning Engineer

- Strong Python programming skills for machine learning development

Machine Learning Expertise :

- Experience implementing classical ML algorithms such as : linear regression, Logistic Regression, Decision Trees, Gradient Boosting

Deep Learning :

- Hands-on experience building Neural Network / Deep Learning models

- Experience with TensorFlow or PyTorch

ML Use Cases (Minimum 2 Required) :

- Recommendation systems

- Image data / computer vision

- Fraud detection / risk modeling

- Price modeling

- Propensity modeling

Natural Language Processing :

- Strong exposure to NLP techniques, including :

- Text generation or classification

- Embeddings

- Similarity models

- User profiling

- Feature extraction from unstructured text

Production ML :

- Experience deploying ML models in production

- Exposure to APIs, CI/CD pipelines, Docker

- Experience working with AWS or GCP environments

Mandatory Filters :

- Mandatory (Company) : Candidates must currently be working in product-based technology companies

- Mandatory (Domain Exclusion) : Candidates from financial institutions / banking / fintech organizations (e.g., large banks, financial services firms) will not be considered

Why Join :

- Work on large-scale real-world ML problems

- Build production AI systems used by thousands of users

- Collaborate with highly skilled product and engineering teams

- Opportunity to drive data-driven product innovation