Sr. Data Scientist

Summary

The Senior Data Scientist will play a pivotal role in shaping the data strategy and driving data-driven decision-making processes within the organization. This position requires a visionary data scientist who collaborate with cross-functional teams, leverage advanced analytical techniques to solve complex business problems and help to mentor junior members of the analytics team. The ideal candidate will have a strong background in data science, excellent interpersonal skills and a passion for innovation.

Duties & Responsibilities

• Collaborate with stakeholders to identify business challenges and opportunities that can be addressed through data science to improve key business outcomes.
• Design, develop, and implement advanced statistical models and machine learning algorithms to extract insights from large datasets.
• Oversee the end-to-end data science project lifecycle, from data collection and preprocessing to model deployment and monitoring.
• Communicate complex analytical concepts and results to non-technical stakeholders in a clear and concise manner.
• Mentor a more junior analysts and data scientists, providing guidance and support in developing analytical models and solutions.
• Stay abreast of the latest industry trends and advancements in data science and incorporate them into the team’s work.
• Collaborate with IT and data engineering teams to integrate data science solutions into existing systems and workflows.
• Partner with Innovation and Digital transformation teams to architect and build analytics engines capable of driving real-time personalized insights using analytic models.
• Apply machine learning models and AI to help mitigate fraud at the credit union.
• Leverage generative AI capabilities and apply solutions to address complex business problems.
• Perform other duties as assigned.

Requirements

Education and Experience Requirements:

• Undergraduate or advanced degree in Mathematics, Statistics, Computer Science, or a related field.
• Knowledge and experience in applying statistical and machine learning methods including regression, classification, clustering, decision trees, and neural networks.
• 6-10 years’ experience in developing, deploying, and managing analytical and machine learning models in a production environment.
• Proven track record of successfully leading data science projects from conception to completion.

Required Knowledge, Skills and Abilities:

• Natural sense of curiosity; being able to self-motivate and proactively find unique or actionable insights and solutions leveraging data.
• Ability to understand business needs and become a leader in enabling data driven business decisions while also having the ability to work successfully in a team environment.
• Extensive experience with machine learning frameworks and libraries (e.g., Scikit-learn, XGBoost).
• Extensive experience in a statistical programming language such as Python or R.
• Experience with building machine learning and AI models in a cloud environment preferably using Databricks
• Excellent problem-solving skills and the ability to think critically and strategically.
• Comfortable working in a dynamic environment with multiple concurrent projects.
• Ability to visualize in advance rough requirements and process necessary to implement new production data science applications.
• Empathy and passion for leading junior team members through complex analytical projects.
• Strong communication skills with ability to present ideas and concepts to diverse audiences.
• Familiarity and experience working with relational databases and SQL.

Preferred Knowledge, Skills and Abilities:

• Data science experience in the financial/banking sector.
• Experience with development tools including Jupyter, PyCharm, VS Code, OpenAI/ChatGPT, Azure DevOps, Jenkins, and Docker.
• Experience with Microsoft Azure cloud environment leveraging ADLS, SQL Server, Cosmos, Azure OpenAI, Cognitive Search, Databricks, and AKS.
• Experience with notable packages/technologies including LLaVA, XGBoost, scikit-learn, Pandas, Spark and GraphQL.
• Familiarity with building data pipelines using unstructured and non-relational data stores.
• Experience writing SQL and developing REST APIs.