Engineering Manager - Data Science

Responsibilities

• Understand business goals and real-world problems in Eruvaka IoT solutions for aquaculture and solve the same using data.
• Lead and guide a team of data scientists and ML engineers, and plan the growth of team members.
• Organize large data sets and build a scalable approach for data analysis.
• Apply robust statistical analysis, causal reasoning, and machine learning techniques to develop, validate, and evaluate models with clear assumptions, confidence intervals, error analysis, and business impact measures.
• Establish metrics and methods to measure and improve performance of Eruvaka solutions (models, algorithms, products).
• Design experiments, A/B tests, observational analyses, and field trials with appropriate control groups, sampling methods, success criteria, statistical power, and bias-mitigation practices.
• Support health and farm intelligence initiatives by working with cross-functional teams to build and evaluate prediction, anomaly detection, early warning, and decision-support models using sensor, feeding, environmental, and field-observation data.
• Define field trial evaluation frameworks, track pre- and post-deployment performance, and partner with domain experts to convert trial results into product and algorithm improvements.
• Work closely with product, cloud, firmware, customer success, field, and domain teams to understand needs, align priorities, and translate business problems into data science work.
• Own and drive the maintenance and development of Eruvaka feeding algorithms, from requirements to field trials.
• Partner with cross-functional teams to convert roadmap needs into data science experiments, pilots, validation methods, and scalable algorithms.
• Be the point of contact for various stakeholders, including customers, management, and internal teams.
• Lead projects from ideation to production.

Qualification, Experience and Required Skills

• Bachelors or higher degree in Computer Science, Statistics, Electrical Engineering, Electronics Engineering, Physics, Mathematics, or related fields.
• Must have delivered at least two production-grade projects.
• Must have strong hands-on experience with programming languages like Python, PySpark and SQL, and frameworks such as NumPy, Pandas, Scikit-learn, etc.
• Should have experience developing and deploying machine learning and deep learning solutions.
• Expertise in classification, regression, time series, decision trees, optimization, etc.
• Engage in MLOps and model development workflows. • Experience working with cloud platforms such as AWS, Azure, or GCP.
• Competence in software development methodologies, code versioning tools such as GitLab, and project tracking tools such as Jira.
• Define best practices, standards, and templates for AI solutions.
• Excellent communication and client/stakeholder management skills
• Preferred experience in agriculture, aquaculture, animal health, biotechnology, IoT, or other sensor-driven domains where models are validated through real-world field data.

Good to have

• Experience with big data tools and familiarity with visualization tools.
• Experience in the area of IoT
• Experience deploying models at the edge.