CarWale - Senior Data Scientist - Machine Learning

Description

Role Overview

Key Responsibilities

Machine Learning & Data Science :

• Design, develop, and deploy advanced machine learning and statistical models to address complex business problems.
• Build, optimize, and maintain end-to-end ML pipelines, including data ingestion, preprocessing, feature engineering, model training, evaluation, and production deployment.
• Lead Generative AI initiatives, leveraging LLMs, diffusion models, and other modern architectures to develop innovative solutions.
• Conduct deep-dive analyses on large and complex datasets to extract actionable insights and recommendations.
• Collaborate with cross-functional teams (Engineering, Product, and Business) to align data-driven initiatives with organizational goals.
  

Engineering & Development

• Write clean, efficient, and maintainable code in Python and at least one additional language (e.g., C#, Go, or Java).
• Implement strong software engineering practices, including version control (Git), CI/CD, testing, and containerization (Docker, Kubernetes).
• Participate in code and model reviews, ensuring adherence to best practices and scalability standards.
• Integrate models into production systems using MLOps tools (e.g., MLflow, Kubeflow, SageMaker, Vertex AI).
  

Innovation & Continuous Improvement

• Stay up to date with the latest developments in Machine Learning, Generative AI, and MLOps frameworks and methodologies.
• Proactively identify opportunities to enhance existing systems and develop new data-driven solutions.
• Maintain detailed documentation for models, workflows, and experiments to ensure transparency and reproducibility.
  

Skills & Qualifications

Education :

• Bachelors or Masters degree in Computer Science, Data Science, Machine Learning, Statistics, or a related quantitative field (PhD preferred).
  

Experience

• 6+ years of hands-on experience in Data Science and Machine Learning roles, with proven experience deploying models to production.
• Demonstrated experience in designing and implementing ML solutions at scale (preferably in cloud environments such as AWS, Azure, or GCP).
• Proven track record of working on Generative AI or LLM-based projects is a strong plus.
  

Technical Skills

• Programming : Proficiency in Python (pandas, NumPy, scikit-learn, PyTorch, TensorFlow); experience with one or more additional languages (e.g., C#, Go, Java).
• Machine Learning : Strong understanding of supervised, unsupervised, and deep learning methods; experience with NLP, computer vision, or generative modeling is desirable.
• Data Engineering : Experience with SQL, data pipelines, and ETL frameworks (e.g., Airflow, dbt, Spark).
• MLOps : Hands-on experience with MLflow, Kubeflow, AWS SageMaker, Vertex AI, or equivalent tools for model tracking and deployment.
• Generative AI : Experience with LLM fine-tuning, embeddings, prompt engineering, and vector databases (e.g., Pinecone, FAISS, Chroma).
• Cloud & DevOps : Familiarity with cloud platforms (AWS, Azure, GCP), containerization (Docker), and orchestration (Kubernetes).
• Visualization : Proficiency with tools like Power BI, Tableau, or Plotly for communicating insights.
  

Soft Skills

• Excellent communication and collaboration skills, with the ability to translate technical findings into business value.
• Strong problem-solving, analytical, and critical-thinking abilities.
• Ability to mentor junior team members and contribute to a culture of innovation and excellence.
  

Preferred Qualifications

• Experience in building AI-powered applications or products.
• Publications, patents, or open-source contributions in ML/AI.
• Familiarity with data governance, model interpretability, and responsible AI principles.