Senior Data Scientist

Description

Job Summary: We are seeking a professional who can translate business challenges into intelligent machine learning (ML)-based solutions, collaborate across various teams to develop and deploy robust models into production, and deliver real-world impact. Key Highlights: 1. Translate business challenges into intelligent machine learning-based solutions 2. Collaborate with teams to identify opportunities and implement models 3. Develop and optimize robust machine learning models **Role Mission** Translate business challenges into intelligent machine learning (ML) solutions. You will work closely with multiple teams (Engineering, Product, Business) to identify opportunities, develop robust models, and deploy them into production—delivering tangible impact and value to our products and customers. **Your Key Responsibilities:** * Problem Solving: Transform business problems into clear, well-defined machine learning projects. * Collaboration: Work alongside Data Engineers, Product Managers, and Business Analysts to identify and validate opportunities where ML can make a difference. * Model Development: 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. * Solution Deployment: Deploy ML models into production, monitor their performance, and ensure they deliver expected value in real-world environments. * Experimentation & Innovation: Conduct experiments, build prototypes (PoCs), and test new approaches to solve complex data-driven problems. * Knowledge Sharing: 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 grounding in statistics, mathematics, and core machine learning principles. * Programming: Proficiency in programming—especially Python and its key 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 (e.g., Git version control, 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 Qualifications:** * 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 Background: Advanced degree (Master’s or PhD) in related fields.