Data Scientist

Job Description

Applied Scientist / ML Engineer (Search & Recommendations) We are looking for a highly skilled Applied Scientist / Machine Learning Engineer to lead the innovation and development of our next-generation Search and Recommendation systems. The ideal candidate will have deep expertise in classical ML, Deep Learning, NLP, and advanced Transformer-based architectures, including BERT and modern Large Language Models (LLMs). Key Responsibilities Search & Recommendation Development Lead the end-to-end design, development, and deployment of search, personalization, and recommendation algorithms. Build systems that significantly enhance user experience and drive measurable business impact. Transformer-Based Model Implementation Apply, fine-tune, and optimize models such as BERT, RoBERTa, and other encoder architectures for: Semantic search Relevance ranking Query understanding Embedding generation Large Language Model (LLM) Innovation Research, prototype, and implement solutions using LLMs. Work on model selection, prompt engineering, LoRA-based fine-tuning, and quantization for efficient inference. Design and implement RAG (Retrieval-Augmented Generation) systems using vector databases and advanced retrieval pipelines. ML Productionization (MLOps) Build, train, validate, and deploy machine learning models into scalable, low-latency production environments. Collaborate with engineering teams to ensure reliability, robustness, and maintainability. Data Strategy & Feature Engineering Partner with Data Engineering to define datasets and develop innovative features for training and evaluation. Ensure data quality and consistency across search and recommendation pipelines. Evaluation & Optimization Define and track KPIs such as NDCG, CTR, latency, perplexity, and other model metrics. Continuously iterate to improve model performance and system quality. Essential Technical Qualifications MS/PhD in Computer Science, Data Science, Engineering, or equivalent experience. Expert-level Python skills; strong knowledge of ML/DL libraries (NumPy, Pandas, etc.) and solid software engineering practices. Deep experience with PyTorch or TensorFlow . Proven hands-on work with Transformer models (BERT, encoder-only models) for IR, NLU, or embedding generation. Practical experience with LLMs , including fine-tuning, deployment, and familiarity with frameworks such as Hugging Face, LangChain, and LlamaIndex. Strong foundational understanding of classical ML algorithms and statistical modeling. Direct experience building or optimizing search ranking systems , recommendation engines, dense retrieval, or vector-based search. Experience with cloud platforms (AWS, GCP, Azure) and MLOps tools such as MLFlow, Kubeflow, Docker, Kubernetes.