Data Scientist / Machine Learning Engineer

Role & responsibilities

• Build ML Models: Design and develop machine learning models using techniques like regression, classification, clustering, deep learning, and NLP to solve real business problems.
• Solve Business Problems: Turn unclear business questions into clear data problems and create smart AI/ML solutions that add value.
• Own the ML Lifecycle: Work on the full journey of ML projects from exploring data and building prototypes to deploying models and improving them over time.
• Work with Data: Analyze and clean structured and semi-structured data to prepare it for modeling.
• Use the Right Algorithms: Apply the best

  ML algorithms

  and understand how they work under the hood.
• Optimize & Automate: Monitor model performance and use MLOps tools to automate testing, deployment, and retraining.
• Collaborate & Communicate: Work closely with engineers, product teams, and business stakeholders. Explain technical ideas in simple terms.
• Stay Updated: Keep learning about new tools, techniques, and trends in AI/ML.
• Use Cloud Tools (Nice to Have): Experience with cloud platforms like GCP and tools like

  Vertex AI is a plus

Skills:

• Strong understanding of

  statistics, probability, and ML algorithms

  .
• Hands-on experience with Python (pandas, scikit-learn, TensorFlow, PyTorch).
• Good with

  SQL

  and data visualization.
• Experience with model evaluation and feature engineering.
• Ability to explain models and results clearly

Bonus Skills

• Experience with deep learning (CNNs, RNNs, Transformers).
• Familiarity with MLOps tools (MLflow, TFX, Docker, Airflow).
• Cloud experience (GCP, AWS, Azure).
• Strong communication and a passion for learning