Senior Data Scientist

Overview

Senior individual contributor role leading end-to-end data science projects for Arrive's AI & Analytics Experience, supporting Sales, Marketing, Finance and Distribution while mentoring other data scientists.

What you'll do

• Lead the end-to-end lifecycle of ML projects, from problem framing, data exploration, feature engineering and model development to deployment and monitoring.
• Partner with cross-functional leadership on joint strategic initiatives to deliver measurable business impact.
• Identify, address and resolve architectural and technical hurdles in developing scalable data science solutions.
• Drive architectural decisions regarding pipelines, model registries or feature store development, ensuring scalability, cost efficiency and reliability.
• Raise the technical bar of the team by mentoring other data scientists and promoting engineering best practices.
• Explore and implement cutting-edge data science technologies to foster continuous team innovation.
• Translate complex data insights into practical business strategies and lead technical discussions with senior stakeholders.
• Mentor junior data scientists and partner with other DNA leads to nurture a data-driven culture and improve data literacy across Arrive.
• Design and prototype Generative AI solutions using LLMs, RAG, embeddings, vector databases, prompt engineering and fine-tuning (LoRA/PEFT).

What you'll need

• At least 5 years of experience working as a Data Scientist, with a proven track record of leading multi-month projects with measurable business impact.
• Strong command of classical ML techniques: regression, classification, ensemble methods, clustering, dimensionality reduction, time-series forecasting, causal inference and optimization techniques.
• Strong expertise in Python and data science libraries.
• Deep proficiency in SQL and cloud architectures and services (e.g., AWS SageMaker, S3, Lambda, ECR, or Snowflake or Airflow).
• Proven experience mentoring other data scientists, raising technical standards and advocating for software development best practices.
• Demonstrated ability to drive architectural decisions (such as pipelines, model registries or feature stores) and represent data science at a senior stakeholder level.
• Solid software engineering foundations or an advanced degree (Masters/PhD) in a relevant field (e.g., machine learning or mobility).
• Hands-on experience with MLOps: model deployment, CI/CD pipelines, monitoring, experiment tracking and containerization (Docker, Kubernetes).
• Practical experience with Generative AI / LLMs: prompt engineering, RAG pipelines, embeddings, fine-tuning, and evaluation frameworks for GenAI systems.
• Excellent communication skills, with the ability to translate technical concepts for business stakeholders in English.

Nice to have

• Experience with geospatial data (e.g., GeoPandas) or big data tools (e.g., PySpark).

Details

• Based in Bengaluru, India.
• Reports to the Manager of Data Science within the DNA function.