Data Scientist

We are seeking a Senior Data Scientist to solve complex business problems through advanced analytics, machine learning, and generative AI. In this role, you will partner closely with business stakeholders, product teams, and engineering to transform large, complex datasets into actionable insights and production-ready AI solutions. The ideal candidate combines deep technical expertise with strong business acumen and excels at communicating complex analytical findings to both technical and executive audiences.

This is a highly collaborative role where you’ll drive projects from initial problem definition through model deployment while helping shape the organization’s data science and AI strategy.

The ideal candidate is naturally curious, highly analytical, and passionate about applying AI and machine learning to solve meaningful business problems. You enjoy working directly with stakeholders, thrive in collaborative environments, and can confidently bridge the gap between advanced analytics and business strategy. You’ll be equally comfortable building production-ready models, communicating with executives, and exploring the latest advancements in Generative AI to drive innovation across the organization., * 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.

• 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., * 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., * 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), Agile Programming Methodologies, Algorithms, Amazon Web Services (AWS), Analysis Skills, Artificial Intelligence (AI), Artificial Intelligence (AI) Agents, Atlassian JIRA, Automation, Best Practices, Business Skills, Business Strategy, Cloud Computing, Communication Skills, Computer Programming, Computer Science, Computer Skills, Consulting, Continuous Deployment/Delivery, Continuous Integration, Data Analysis, Data Processing, Data Science, Data Sets, Data Warehousing, Decision Support, DevOps, Distributed Computing, Docker, Emerging Technology, Experiment Design, Financial Services, Git, Healthcare, Internet Security, Machine Learning, Mathematics, Mentoring, Metrics, Microsoft Windows Azure, Model Validation, Modeling Languages, Multitasking, Neo4j, Network Performance/Analysis, Presentation/Verbal Skills, Problem Solving Skills, Process Improvement, Product Engineering, Production Machining, Project/Program Management, Python Programming/Scripting Language, Relational Databases (RDBMS), Reporting Dashboards, Requirements Management, SQL (Structured Query Language), SQL Databases, Software Development, Software Engineering, Statistical Modeling, Statistics, Team Player, Trend Analysis, Web Analytics

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