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.