Head of Data Science & Remote Sensing

Description :

As Head of Data Science & Remote Sensing, you will own the building, designing and execution of the Groups in house engine remote sensing and Earth Observation (EO) strategy. You will lead a multidisciplinary team of data scientists, remote sensing engineers, and geospatial analysts to convert multi-sensor satellite imagery (SAR, optical, thermal) and weather/soil datasets into farmer-ready insights. Your mandate is to build scientifically rigorous yet production-grade pipelines that power the products while mentoring talent and institutionalizing calibration, validation, and quality assurance frameworks.

High Output Management Principles Applied

- Focus on managerial leverage : hire exceptional leads, establish rituals, and eliminate bottlenecks.

- Define output metrics : uptime, farmer adoption, model accuracy, data pipeline SLAs.

- Task-relevant maturity : delegate decisions based on individual competence & experience.

- Build scalable systems instead of relying on individual heroics.

Job Summary :

Builds and scales EO/RS data pipelines for agriculture, ensuring robust scientific credibility and operational deployment. Owns end-to-end pipelines from raw SAR/optical/thermal imagery to calibrated farmer insights, powering Products with accuracy, reliability, and interpretability.

Roles & Responsibilities :

- Architect, build, and scale pipelines : Sentinel-1 SAR preprocessing, optical fusion, gap-filling, ET estimation (SEBAL/energy balance), soil moisture, stress detection, and crop phenology.

- Own EO/ML model lifecycle : design, training, validation, deployment, monitoring, drift detection, and retraining cadence.

- Define validation metrics(R/MAE/F1/mAP),; establish calibration plans, ground-truth sampling, and error budgets for each crop and district.

- Partner with product and engineering to expose insights via APIs and app dashboards; ensure farmer-facing confidence thresholds.

- Stand up rigorous Cal/Val (Calibration/Validation) programs : agronomy inputs, field data partnerships, and A/B experiments.

- Recruit, mentor, and scale a team of EO/ML scientists; drive OKRs, documentation, and knowledge-sharing.

- Establish labeling/QA SOPs, build golden datasets, and adopt active learning for annotation efficiency.

- Manage compute and storage tradeoffs; design for scalability while controlling costs.

- Lead collaborations with academia, space agencies, and EO labs to accelerate innovation and maintain technical edge.

- Architect, build, and scale multi-sensor satellite data pipelines (SAR, optical, thermal), ground ingestion, preprocessing, fusion, and real-time analytics.

- Facilitate buildbuypartner decisions; manage vendors and strategic tech partnerships (academia, space agencies, hyperscalers).

- Stand up rigorous Cal/Val programs with ground truth, agronomy inputs, and A/B experimentation frameworks

- Translate business needs into technical roadmaps and quarterly release plans; align with CEO/CTO and Business Heads

Candidate Profile & Skill Requirements :

- Leadership & Scale : 8+ years in Earth Observation and Remote Sensing applied to agriculture.

- Strong expertise in SAR, optical, and thermal satellite data processing : Sentinel-1/2, Landsat, MODIS Proficiency in SEBAL/energy balance models, ET estimation, stress/yield proxies.

- Hands-on experience with geospatial stacks : GDAL/Rasterio, xarray/dask, GeoPandas, PostGIS, STAC catalogs.

Technology Leadership :

- Skilled in PyTorch/TensorFlow; ML pipeline development with MLflow/W&B; validation at scale.

- Demonstrated success in deploying ML for EO : time-series analysis, computer vision, deep learning on geospatial data.

- Experience with retraining strategies, drift management, and large-scale error calibration.

MLOps & Platforms :

- Proficient in Airflow/Prefect, Docker/Kubernetes, scalable inference design.

- Strong knowledge of COG/Zarr formats, cloud-native data management, and API delivery of insights.

Innovation & IP :

- Demonstrated experience in scientific publications, patents, or invention disclosures in EO/ML.

- Proven track record of building novel methods for EO data analysis and validation.

Industry Knowledge (Preferred) :

- AgTech analytics and parametric/index insurance data pipelines.

- Rainfall downscaling, crop simulation model coupling, and ground-truth calibration.

- Operational constraints of EO systems : revisit cycles, cloud interference, latency management.

Personal Attributes :

- Hands-on scientist with the ability to think strategically.

- Integrity, resilience, and ownership mindset.

- Highly organized, detail-oriented, frugal with compute, and results-focused.