Senior Data Scientist
Pune, IndiaFull-TimeSeniorAI / Data Science
Responsibilities
- Data Science Customer Success & Problem Solving: Act as the senior technical point of contact, driving successful outcomes by identifying, scoping, and leading data science projects that directly address high-value customer problems. Measure and report on the business impact (ROI, efficiency gains) of deployed models. Ensure rigorous testing and validation of all new data science features and model deployments.
- Technical Design & Implementation: Design, implement, monitor, and maintain scalable, production-grade Data Science systems. Research and evaluate the latest in Machine Learning, Generative AI, and advanced statistical models for new feature development. Actively scout, evaluate, and implement the latest advancements in Data Science, Machine Learning (MLOps, LLMs/Generative AI), and AI to maintain Aera’s technical edge.
- Data Science Technical Support: Take ownership of critical model performance issues. You will champion rapid resolution for Data Science models and pipelines, working collaboratively with Support, Customer Success, and Product teams to implement robust, long-term technical solutions.
- Product Collaboration: Act as a technical advisor to the Product Team by sharing insights from field implementations. You will provide data-driven feedback on model performance and usability to help inform future improvements to Aera Decision Cloud’s Data Science capabilities.
About You
- We are looking for a hands-on practitioner—someone who is deeply technical, customer-focused, and passionate about engineering excellence.
- Customer Focus: You are someone who is obsessed about solving customer problems and delivering tangible business value using Data Science, understanding the criticality of timely resolution and deployment.
- Technical Foundation: Master’s or Bachelor’s Degree in Computer Science, Engineering, Mathematics, Statistics, or a related quantitative field with a focus on Data Science/Machine Learning/Deep Learning/AI.
- Strong grounding in core ML concepts—bias-variance tradeoffs, feature engineering, model evaluation, uncertainty quantification—not just framework/API familiarity.
- Experience: 7+ years of experience as a Data Scientist or Machine Learning Engineer, ideally in a B2B SaaS or product development setting.
- Modern ML/AI Expertise: Proven, hands-on experience designing, developing, and deploying systems utilizing the latest advancements in AI (e.g., MLOps practices, Generative AI, vector databases, or transformer models).
- Production Deployment: Strong experience with the full ML lifecycle, including translating research models into scalable, production-ready services using modern software development practices.
- Technical Communication: Ability to distill complex Data Science concepts into clear, actionable insights for product managers, customer executives, and cross-functional teams.
- Technical Stack:
- Exceptional proficiency in SQL and Python; able to write high-quality, high-performance, and maintainable code.
- Experience with distributed systems and ML frameworks such as Dask, Ray, or Spark.
- Familiarity with production ML services like Kubeflow, Sagemaker, or similar cloud-native MLOps tools is a significant advantage.
