Data Scientist/Machine Learning Engineer

** As a Data Scientist / Machine Learning Engineer at our company, you will play a crucial role in building and deploying machine learning solutions that have a direct impact on customer experience, personalization, and revenue growth. You will collaborate 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 position offers an exciting opportunity to work on real-world ML applications like recommendation systems, personalization engines, pricing optimization, and fraud detection in a dynamic product company environment. **

** - Build and deploy production-grade ML models: - Develop machine learning models for personalization, recommendation systems, and user ranking - Create 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 like text classification, embeddings, and similarity models - Construct 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 seeking a strong Data Scientist / Machine Learning Engineer with hands-on experience in building real-world ML systems. **Mandatory

** - 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 **Additional Details:** - 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 **Job Description:** **

** As a Data Scientist / Machine Learning Engineer at our company, you will play a crucial role in building and deploying machine learning solutions that have a direct impact on customer experience, personalization, and revenue growth. You will collaborate 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 position offers an exciting opportunity to work on real-world ML applications like recommendation systems, personalization engines, pricing optimization, and fraud detection in a dynamic product company environment. **

** - Build and deploy production-grade ML models: - Develop machine learning models for personalization, recommendation systems, and user ranking - Create 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 - Extr