What are the responsibilities and job description for the Sr. Data Scientist position at CommunityAmerica Credit Union?
Sr. Data Scientist
2373
Type
Full-Time
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.
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