Data Scientist

at  Soft dev team

At Agile Dream Team, we leverage data and AI to create innovative solutions that drive business success. 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. Learn more about us at: www.agiledreamteam.com

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.

ROLE OVERVIEW

As a Data Scientist, you will be responsible for building, deploying, and optimizing machine learning models, conducting data analysis, and creating predictive solutions that enhance business intelligence and AI-driven applications. You will work closely with Data Engineers, AI Engineers, and Product Managers to develop 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.