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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.
This role focuses on leveraging data and AI to create innovative solutions that drive business success.
You will collaborate with Data Engineers, AI Engineers, and Product Managers to build scalable and impactful data solutions.
Key Responsibilities
Develop and deploy machine learning models for predictive analytics, recommendation systems, NLP, and computer vision.
Perform statistical analysis and data exploration to extract actionable insights.
Design and implement AI/ML algorithms, including supervised and unsupervised learning techniques.
Utilize Deep Learning frameworks (TensorFlow, PyTorch) for complex AI tasks.
Work with big data processing frameworks such as Apache Spark and Dask.
Collaborate with Data Engineers to optimize data pipelines and feature engineering.
Implement model monitoring, validation, and optimization techniques.
Use A/B testing and experimentation to refine models and improve decision-making.
Deploy AI solutions in cloud environments (AWS, Azure, GCP).
Stay updated with AI/ML research trends and integrate state-of-the-art techniques into business applications.
Requirements
Required Skills & Experience
Proficiency in AI/ML frameworks: TensorFlow, PyTorch, Scikit-learn.
Strong understanding of supervised, unsupervised, and deep learning models.
Experience in data analysis and visualization (Pandas, NumPy, Matplotlib, Seaborn).
Hands-on experience with SQL and NoSQL databases for data retrieval and processing.
Proficiency in Python, R, or Julia for machine learning and data analysis.
Experience with cloud AI services (AWS SageMaker, Google Vertex AI, Azure ML).
Strong knowledge of time-series forecasting, NLP, and recommendation systems.
Experience in A/B testing, experimentation, and model performance evaluation.
Familiarity with MLOps tools (MLflow, Kubeflow) for model deployment and tracking.
Preferred Qualifications
Experience with Large Language Models (LLMs) and generative AI.
Hands-on knowledge of Retrieval-Augmented Generation (RAG) and prompt engineering.
Strong background in graph analytics, anomaly detection, and optimization algorithms.
Understanding of graph databases (Neo4j, ArangoDB) and knowledge graphs.
Contributions to open-source AI/ML projects or academic research.
