Lead Data Scientist

Systems Innovation Engineering•Saint Paul, MN

1d•Hybrid

About The Position

The Lead Data Scientist is responsible for designing, developing, testing, and maintaining software applications to provide advanced analytics for software and edge solutions team. This role focuses on developing innovative solutions driven by exploratory data analysis from complex and high-dimensional datasets. The Data Scientist will apply knowledge of statistics, machine learning, programming, and data modeling. They use a flexible, analytical approach to design, develop, and evaluate predictive models and advanced algorithms to mine the stores of big data. They generate and test hypotheses and analyze and interpret the results of product experiments. Create data visualizations, dashboards, and reports to communicate findings. This role will involve applying AI/ML techniques to read and process technical data to include CAD models for ingestion into enterprise systems. The Journeyman Data Scientist will work with product engineers to translate prototypes into new products, services, and features and provide guidelines for large-scale implementation. This role will require collaboration with other data scientists and software engineers to implement algorithms and provide insights to end users. The ideal candidate has at least 5 years of experience in data science, with a demonstrated ability to collaborate with cross-functional teams.

Requirements

Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, or related quantitative field.
At least 5 years of experience in data science or related discipline.
Experience analyzing system performance data and time series data.
Proficiency in Python and its data science libraries (Pandas, NumPy, Scikit-learn, etc.).
Strong background in statistical analysis, forecasting techniques, and anomaly detection
Experience with SQL and relational databases.
Knowledge of data visualization tools for creating operational dashboards (Tableau, Power BI, Grafana, or similar).
Understanding of machine learning fundamentals with a willingness to expand AI/ML implementation skills.
Excellent problem-solving and analytical skills.
Superior written and verbal communication skills.

Nice To Haves

Experience working with CAD models and other technical data sets
Experience researching, developing, and implementing machine learning algorithms and models for tasks such as classification, regression, clustering, anomaly detection, and recommendation systems
Familiarity with stream processing of real-time data
Knowledge of cloud platforms (AWS, Azure, or GCP) and their monitoring services
Experience with ML frameworks (TensorFlow, PyTorch, or similar)
Familiarity with version control systems (Git)
Experience with containerization and orchestration tools (Docker, Kubernetes)
Exposure to data streaming applications (Confluent, Kafka, RabbitMQ, or similar)
In-depth understanding of the aerospace and defense industry.

Responsibilities

Lead the design, development, and implementation of statistical techniques and algorithms.
Write clean, maintainable, and efficient code, and ensure best practices in coding standards.
Test algorithms against key benchmarks and implement techniques to enhance performance.
Work with developers, solution architects, and DevSecOps engineers to incorporate algorithms within software architectures and effectively deploy the algorithms within existing code base.
Work with engineers to incorporate prototype algorithms into new products, services, and features and provide guidelines for large-scale implementation.
Research and identify areas for applications of AI/ML in support of the program.
Work with the development team and government teams to align AI/ML efforts.
Prepare and maintain comprehensive technical documentation related to algorithm development.
Ensure accuracy and completeness of all documentation.
Mentor junior data scientist and coordinate activities to complete key data science tasks
Foster effective collaboration with cross-functional teams to achieve project objectives.
Communicate complex technical information clearly and effectively.
Utilize advanced software development tools and methodologies to support project requirements.
Integrate software development tools and methodologies into the workflow to improve efficiency and accuracy.