Data Scientist - NLP/Computer Vision

Role summary: You will own the end-to-end delivery of ML- and data-driven capabilities for clientsturning business needs into production-grade data pipelines, deployed models (recommendation / NLP / GenAI), and measurable outcomes. This is a builder + owner role: you will be accountable for technical delivery, operational reliability, and proactive stakeholder expectation management. Core responsibilities (delivery-oriented): - Own the end-to-end delivery of production-grade ML systems for recommendations, NLP, and personalizationdriving outcomes from problem definition and metric design to deployment, monitoring, and continuous improvement based on real-world impact. - Take full ownership of scalable data pipelines. Architect, build, and operate robust ETL/ELT systems that transform disparate data sources into high-quality, analytics- and ML-ready datasets, ensuring reliability for business-critical decisions. - Establish and enforce data architecture standards. Design scalable storage patterns for analytics and ML, including data lake implementations, with strict governance on partitioning, schema evolution, file optimization, and efficient use of columnar formats such as Parquet. - Drive MLOps as a discipline, not an afterthought. Build and institutionalize CI/CD-aligned ML workflows, including automated testing, reproducibility, deployment infrastructure, and monitoringensuring models operate reliably in production environments. - Deliver production-ready systems, not prototypes. Expose model and data capabilities through versioned APIs and services aligned with microservices architecture, enabling seamless integration into real-world applications. - Own stakeholder outcomes and client success. Lead problem discovery, define scope and constraints, translate business needs into scalable technical solutions, and proactively manage expectations through clear communication, risk mitigation, and measurable success criteria. Must-have Experience: - End-to-end ownership of at least one production ML deliverable (recommender, NLP, personalization, segmentation, or GenAI feature), including how it was evaluated and what changed after launch. - Hands-on experience in developing and deploying Computer Vision/Image Recognition solutions (e.g., image classification, object detection, OCR, image segmentation, or multimodal vision models) using frameworks such as OpenCV, PyTorch, TensorFlow, YOLO, or Hugging Face Vision models in production environments. - Hands-on delivery of scalable data pipelines, including concrete scale/complexity details (volumes, SLAs, latency/freshness targets, backfill strategy, failure handling). - Production software practices: testing discipline, CI/CD usage, API design, operational monitoring, and incident learnings relevant to data/ML services. - Client-facing delivery: examples where they translated business requirements into technical plans, handled scope changes, and aligned stakeholders on measurable success criteria. - Practical data-lake experience (design choices, columnar formats like Parquet/ORC, query/performance or cost trade-offs). Soft Skills & Experience & Qualifications: - Operate effectively in both independent and collaborative environments, taking ownership of outcomes without dependency on constant direction. - Exhibit strong written and verbal communication skills, with the ability to articulate complex ideas clearly to both technical and non-technical stakeholders. - Demonstrate sharp critical thinking and structured problem-solving abilities, with a questioning mindset that challenges assumptions and drives better decisions. - Show a high degree of ownership and adaptability, proactively stepping in to address gaps rather than deferring responsibility. - Communicate insights with clarity and impact, especially when translating data into actionable business recommendations. - Thrive in fast-paced environments, balancing flexibility with a solution-oriented, execution-focused approach. - Bachelors degree in computer science, Data Analytics, Engineering, or a related discipline. - 4-6 years of hands-on experience in building and delivering data science and data engineering solutions in production environments. - Strong grasp of core data engineering principles, including data lake architectures, columnar storage formats, ETL/ELT frameworks, and BI ecosystems.