Senior Data Scientist

Machine Learning SpecialistWe are seeking an experienced Machine Learning Specialist to design, build, deploy and optimize ML/AI solutions across a range of business problems. The ideal candidate understands the full ML lifecycle from data pre-processing and experimentation to deployment, monitoring, and continuous improvement.The role requires strong engineering fundamentals, modeling expertise and practical knowledge of MLOps, along with the ability to convert business use cases into scalable ML pipelines.Key Responsibilities:Build, train, and evaluate machine learning models (classification, regression, clustering, NLP, deep learning).Develop custom models or adapt pre-trained models (transfer learning).Conduct feature engineering, feature selection, and data transformation.Perform hyperparameter tuning using tools like Optuna, Ray Tune, or built-in frameworks.Evaluate model performance using appropriate metrics (AUC, F1, MAE, BLEU, WER, etc.).Data Engineering & Processing:Work with structured and unstructured data (text, images, audio, logs, documents).Build data pipelines using Python, SQL, Spark, Airflow, or similar.Apply data quality checks, deduplication, anomaly detection, and data validation (Great Expectations, pydantic).ML Deployment & MLOps:Deploy models as APIs using FastAPI, Flask, Node, or cloud-native tools.Implement CI/CD pipelines for ML (GitHub Actions, GitLab, Jenkins).Use containerization & orchestration (Docker, Kubernetes).Manage models using MLflow, Vertex AI, SageMaker, Azure ML, or Databricks.Implement automated retraining, model versioning, rollback, and monitoring.Handle inference optimizations (quantization, batching, caching).Languages & GenAI:Work with foundation models like GPT, LLaMA, Claude, Mistral, etc.Build RAG pipelines with vector databases (Qdrant, Pinecone, FAISS, Elasticsearch).Prompt engineering and evaluation.Optimize embeddings, context windows, and chunking strategies.Integrate LLMs into production APIs and workflows.Cloud & Infrastructure:Experience with at least one cloud provider (AWS, Azure, GCP).Use managed ML services like: AWS: SageMaker, Lambda, Textract, Comprehend Azure: AI Foundry, Functions, Search GCP: Vertex AI, Cloud RunManage GPU/CPU compute workloads efficiently.Software Engineering Best Practices:Write clean, modular, well-tested Python code.Maintain repositories adhering to branching, versioning, and code review standards.Use design patterns for scalable ML pipelines.Build internal tools for automation, testing, and monitoring.Cross-functional Collaboration:Work closely with product managers, data engineers, backend engineers, and business analysts.Translate business problems into ML solutions with measurable ROI.Document technical designs, APIs, models, architecture diagrams.Monitoring, Observability & Optimization:Implement model monitoring for data drift, concept drift, performance degradation.Evaluate model reliability, bias, fairness.Implement logging & metrics (Prometheus, Grafana, ELK).Continuously improve model performance and resource usage.Requirements:Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or equivalent experience.Experience building ML products end-to-end.Experience in domains like FinTech, Healthcare, Retail, Manufacturing, or Real Estate is a plus.Contributions to open-source ML frameworks are a bonus.Ours Ethos:Transparent: This isn't about having access to everything; it's about fostering an environment where relevant information is proactively shared and easily accessible to our team and clients.Resourceful: We embrace creative thinking and encourage our team to explore inventive ways to overcome obstacles while considering the most efficient use of available resources.United: Our success is recognizing that what we do is a collective effort.Excellence: Excellence is the driving force behind everything we do.We are a proud equal opportunity employer, dedicated to promoting diversity, equity, and inclusion in our hiring practices and workplace culture.