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Engineering","optional":true},{"name":"RAG","optional":true}],"status":"live","first_seen_at":"2026-11-01T11:53:21Z","employer_posted_date":null,"last_verified_at":"2026-11-01T11:53:21Z","board_verified":false,"closed_at":null,"days_open":5,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":5},"description":"We are looking for a highly skilled Data Scientist to develop advanced models, analyze complex datasets, and generate insights that fuel decision-making and automation.\nThis role focuses on leveraging data and AI to create innovative solutions that drive business success.\nYou will collaborate with Data Engineers, AI Engineers, and Product Managers to build scalable and impactful data solutions.\nKey Responsibilities\nDevelop and deploy machine learning models for predictive analytics, recommendation systems, NLP, and computer vision.\n\nPerform statistical analysis and data exploration to extract actionable insights.\n\nDesign and implement AI/ML algorithms, including supervised and unsupervised learning techniques.\n\nUtilize Deep Learning frameworks (TensorFlow, PyTorch) for complex AI tasks.\n\nWork with big data processing frameworks such as Apache Spark and Dask.\n\nCollaborate with Data Engineers to optimize data pipelines and feature engineering.\n\nImplement model monitoring, validation, and optimization techniques.\n\nUse A/B testing and experimentation to refine models and improve decision-making.\n\nDeploy AI solutions in cloud environments (AWS, Azure, GCP).\n\nStay updated with AI/ML research trends and integrate state-of-the-art techniques into business applications.\n\nRequirementsRequired Skills & Experience\nProficiency in AI/ML frameworks: TensorFlow, PyTorch, Scikit-learn.\n\nStrong understanding of supervised, unsupervised, and deep learning models.\n\nExperience in data analysis and visualization (Pandas, NumPy, Matplotlib, Seaborn).\n\nHands-on experience with SQL and NoSQL databases for data retrieval and processing.\n\nProficiency in Python, R, or Julia for machine learning and data analysis.\n\nExperience with cloud AI services (AWS SageMaker, Google Vertex AI, Azure ML).\n\nStrong knowledge of time-series forecasting, NLP, and recommendation systems.\n\nExperience in A/B testing, experimentation, and model performance evaluation.\n\nFamiliarity with MLOps tools (MLflow, Kubeflow) for model deployment and tracking.\n\nPreferred Qualifications\n Experience with Large Language Models (LLMs) and generative AI.\n\nHands-on knowledge of Retrieval-Augmented Generation (RAG) and prompt engineering.\n\nStrong background in graph analytics, anomaly detection, and optimization algorithms.\n\nUnderstanding of graph databases (Neo4j, ArangoDB) and knowledge graphs.\n\nContributions to open-source AI/ML projects or academic research.\n\nOriginally posted on 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