Senior Data Scientist

Description

Job Summary: Translate business challenges into intelligent Machine Learning (ML)-based solutions, collaborating across teams to develop, deploy, and monitor models in production. Key Highlights: 1. Solve business problems using Machine Learning 2. Develop, optimize, and deploy ML models into production 3. Collaborate and innovate on data-driven solutions **Role Mission** Translate business challenges into intelligent Machine Learning (ML) solutions. You will closely collaborate with various teams (Engineering, Product, Business) to identify opportunities, build robust models, and deploy them into production—delivering real impact and tangible value to our products and customers. **Key Responsibilities:** * Solve Problems: Transform business problems into clear, well-defined Machine Learning projects. * Collaborate: Work closely with Data Engineers, Product Managers, and Business Analysts to identify and validate opportunities where ML can make a difference. * Develop Models: Design, train, validate, and optimize Machine Learning models (using techniques such as decision trees, neural networks, supervised/unsupervised learning, etc.), with emphasis on feature engineering quality and critical analysis of results. * Implement Solutions: Deploy ML models into production, monitor their performance, and ensure they deliver expected value in real-world environments. * Experiment and Innovate: Conduct experiments, develop prototypes (PoCs), and test new approaches to solve complex problems using data. * Share Knowledge: Create technical documentation, share learnings, and contribute to the company’s data and ML culture—potentially through internal presentations or training sessions. **What We Expect From You:** * Strong Fundamentals: Excellent foundation in statistics, mathematics, and core Machine Learning principles. * Programming: Proficiency in programming, especially Python and its primary Data Science libraries (e.g., Pandas, NumPy, Scikit-learn). * Practical Experience: Proven experience building and deploying ML models in real-world projects using popular frameworks (e.g., TensorFlow, PyTorch, XGBoost, Scikit-learn). * Best Practices: Familiarity with software development best practices (version control with Git, testing) and MLOps. Awareness of performance optimization (CPU/GPU, memory, I/O). * Mindset: Proactivity, curiosity, analytical thinking, and a strong desire to understand how ML/AI can positively impact the business. * Communication: Ability to clearly communicate complex ideas to diverse audiences. * Education: Bachelor’s degree completed in Computer Science, Engineering, Statistics, Mathematics, Physics, or related fields. **Nice-to-Have:** * Cloud Computing: Experience with cloud platforms, especially Google Cloud Platform (GCP) and its services (Vertex AI, BigQuery, Dataflow, Dataproc). * NLP: Knowledge of and/or experience with Natural Language Processing. * Big Data: Experience processing large-scale data using tools like Spark, Beam, or Dask. Familiarity with modern Data Lakes and Data Warehouses. * AI Ethics: Awareness of ethical implications, privacy concerns, and explainability (XAI) in AI usage. * Academic: Graduate degree (Master’s or PhD) in related fields.