Data Scientist

Roles & Responsibilities:

• Work in collaboration with product owners, applied data scientists, MLOps, geospatial experts, platform engineers to envision solutions to real-world, ambiguous business use-cases with low latency/ high throughput.
• Focus on identifying and solving customer problems with simple and elegant solutions, while working backwards from customer requirements.
• Quickly propose and validate hypotheses to direct the product roadmaps. Own time-bound, End-to-End (E2E) solutioning of large-scale ML applications, ranging from resource, requirements gathering, data collection, cleaning and annotation, model development, validation, deployment, monitoring, etc.
• Brainstorm, deep dive, implement, debug into fundamentals of the systems (e.g., architectures, losses, efficiency, serving etc), while writing clean, production level code, and conduct A/B as necessary.
• Define proper output Data Science metrics and calibrate them to the desired business metrics.
• Clearly communicate findings verbally and in writing to stakeholders of varied backgrounds. Have attention to detail.
• Engage and initiate collaborative efforts to meet ambitious (applied research and product/client delivery) goals.
• Innovate and advance State-Of-The-Art (SOTA) in-house solutions, and communicate findings as IPs (patents, papers), as deemed applicable by business.
• Mentor junior staff, interns as applicable. Assist Science managers in effective project, resource management, hiring, and timely deliverables (in an agile manner), via showcasing strong sense of ownership and accountability.

Educaton :

• M.Tech/MS (Research)/PhD in CS, EE, EC, Remote Sensing, or related fields preferably from leading academic/industrial labs/institutes/corporates.
• Exceptional undergraduates with strong research/industry experience will also be considered.

Experience :

• 4+ years of applied ML/Computer Vision experience (industry preferred).
• Proven experience taking ML models from POC → production → monitoring.

Must have Technical Expertise:

• A proven track record of relevant experience in computer vision, NLP, learning theory, optimization, ML+Systems, foundational models, etc.
• Technically familiar with some, or most of (as evidenced by problem solving skills in novel scenarios): Transformers, UNet, RNNs/LSTMs/GRUs, YOLO/RCNN/Encoder–Decoder architectures, Generative models (GAN, VAE, Diffusion), Self-supervised & contrastive learning, Representation learning, domain adaptation & generalization, Semi-/Active learning, noisy-label learning, Super-resolution, anomaly detection, clustering, Model compression: distillation, pruning, quantization.
• PyTorch, Python, SQL, distributed systems (Spark), MLOps for large-scale training, data pipelines, and deployment.

Good to Have:

• SAR (VV/VH), NDVI, FCC, multispectral optical data, Temporal modeling (SITS, forecasting, seasonal dynamics), Cross-modal fusion (SAR+Optical, EO+tabular/ground)
• First-authored publications in ICLR, NeurIPS, CVPR, ICCV, ECCV, ICML, AAAI, IGARSS, IEEE TGRS, etc.
• Experience with geospatial datasets, climate models, foundation models, or EO analytics.

Benefits:

• Medical Health Cover for you and your family including unlimited online doctor consultations
• Access to mental health experts for you and your family
• Dedicated allowances for learning and skill development
• Comprehensive leave policy with casual leaves, paid leaves, marriage leaves, bereavement leaves

Interview Process:

• Intro call
• Assessment
• Presentation
• Interview rounds (ideally up to 3-4 rounds)
• Culture Round / HR round