CarWale - Senior Data Scientist - Machine Learning

Job Description Description Senior Data Scientist (Machine Learning Engineer) Role Overview We are seeking a highly skilled Senior Data Scientist / Machine Learning Engineer to design, build, and deploy advanced machine learning and generative AI solutions that drive business value. This role blends deep technical expertise with strategic thinking, requiring hands-on experience in data science, software engineering, and MLOps. 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. (ref:hirist.tech)