Description

• Design, develop, validate, and deploy machine learning and statistical models that solve real-world business challenges.
• Build and implement Generative AI solutions using large language models (LLMs), prompt engineering, retrieval-augmented generation (RAG), and agentic AI workflows where appropriate.
• Analyze large-scale structured and unstructured datasets to identify trends, patterns, and opportunities for business optimization.
• Develop interactive dashboards and analytical applications using Plotly Dash to communicate insights and support executive decision-making.
• Partner with business stakeholders to understand objectives, define success metrics, and translate business requirements into scalable analytical solutions.
• Perform statistical analysis, hypothesis testing, experimentation, and model validation to ensure analytical accuracy and reliability.
• Deploy machine learning models into cloud environments and collaborate with engineering teams to operationalize AI solutions.
• Present findings and recommendations to technical teams, business leaders, and executive stakeholders in a clear, concise manner.
• Mentor junior data scientists and contribute to best practices across data science, machine learning, and AI development.
• Continuously evaluate emerging AI technologies and recommend innovative approaches that improve business outcomes.

Required Qualifications

• Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or another quantitative discipline (Master's or PhD preferred).
• 6+ years of professional experience in Data Science, Machine Learning, Applied AI, or Advanced Analytics.
• Expert-level Python programming skills for data analysis, machine learning, and automation.
• Strong experience with Pandas, NumPy, and PySpark for processing and analyzing large datasets.
• Advanced SQL skills with experience querying complex relational databases and data warehouses.
• Strong foundation in statistics, probability, experimental design, regression, classification, clustering, and hypothesis testing.
• Experience designing, training, evaluating, and deploying production machine learning models.
• Hands-on experience building interactive dashboards using Plotly and Plotly Dash.
• Experience working with cloud platforms such as AWS, Azure, or Google Cloud for analytics and model deployment.
• Excellent communication and presentation skills with the ability to explain complex technical concepts to non-technical stakeholders.
• Proven ability to manage multiple projects while partnering effectively across business and technical teams.

Preferred Qualifications

• Experience developing Generative AI applications using LLMs, prompt engineering, RAG, vector databases, AI agents, and orchestration frameworks such as LangChain or LlamaIndex.
• Experience working with graph databases (Neo4j, Amazon Neptune, etc.) and graph analytics.
• Knowledge of network analysis, graph algorithms, and knowledge graph architectures.
• Experience with MLOps, model monitoring, CI/CD pipelines, and ML lifecycle management.
• Experience with distributed computing frameworks and large-scale data platforms.
• Experience in financial services, cybersecurity, healthcare, or other highly regulated industries.
• Familiarity with containerization technologies such as Docker and Kubernetes.
• Experience working in Agile development environments using Jira, Git, and modern software engineering practices.

Technical Environment

• Programming: Python, SQL
• Data Processing: Pandas, PySpark, NumPy
• Machine Learning: Scikit-learn, XGBoost, TensorFlow, PyTorch (or similar)
• Generative AI: LLMs, Prompt Engineering, RAG, AI Agents, LangChain, LlamaIndex
• Visualization: Plotly, Plotly Dash
• Cloud: AWS, Azure, or Google Cloud
• Databases: SQL, Graph Databases (Neo4j/Neptune preferred)
• DevOps & MLOps: Git, CI/CD, Docker, Kubernetes, MLflow (preferred)

Key Skills

Education

Bachelor's degree