Data Scientist Skill Level 3

Wyetech is seeking an experienced Senior Data Scientist / MLOps Engineer (DS3) to support the MT&E team. The ideal candidate will possess a strong background in Data Science, Machine Learning Operations (MLOps), and advanced analytics, with demonstrated experience developing, optimizing, and transitioning machine learning solutions into production environments.

This position focuses on developing scalable analytical capabilities, automating data-driven workflows, and integrating machine learning models into operational systems to support mission-critical objectives.

Required Skills/

Experience:

• Significant professional experience in Data Science, Machine Learning, and MLOps.

• Experience designing, developing, training, evaluating, and deploying machine learning models and advanced analytical algorithms.

• Experience implementing statistical analysis, data mining, artificial intelligence, and predictive analytics.

• Proficiency with data analysis and scientific programming tools such as Python, R, SAS, or MATLAB.

• Experience developing feature engineering processes, data preprocessing workflows, and analytical pipelines.

• Experience evaluating and optimizing model performance using cross-validation, ROC curves, confusion matrices, and other standard metrics.

• Experience integrating machine learning models and analytical prototypes into production environments.

• Understanding of scalable data processing, software engineering principles, and cloud-based analytical solutions.

• Ability to collaborate with software engineers, cloud developers, customers, and subject matter experts to translate mission requirements into operational analytics.

• Experience leading analytical development efforts, providing technical guidance, and supporting multiple concurrent initiatives.

Responsibilities and Capabilities:

• Develop machine learning, statistical, data mining, and graph-based algorithms to identify patterns and generate actionable insights from complex datasets.

• Collaborate with subject matter experts to extract relevant information from structured and unstructured data sources, including SQL databases, metadata, and network logs.

• Develop feature vectors, optimize model parameters, and select appropriate analytical techniques based on dataset characteristics and performance requirements.

• Translate qualitative customer requirements into quantitative analytical models, algorithms, and software prototypes.

• Design and execute statistical tests, experiments, and simulations to support data-driven decisions when training or example datasets are limited.

• Produce data visualizations and analytical reports that communicate findings and support operational decision-making.

• Develop and train machine learning systems that enable mission automation and improve analytical efficiency.

• Lead and oversee multiple analytical development efforts, providing technical direction, assigning responsibilities, and monitoring performance.

• Collaborate with software engineering and cloud development teams to transition prototype analytics into scalable production systems.

• Evaluate emerging machine learning and pattern recognition technologies and guide their integration into mission solutions.

Qualifications:

Candidates must meet one (1) of the following education and experience combinations:

• Bachelor's degree in a quantitative discipline plus ten (10) years of relevant experience.

• Master's degree in a quantitative discipline plus eight (8) years of relevant experience.

• Doctoral degree in a quantitative discipline plus six (6) years of relevant experience.

• An additional four (4) years of qualifying experience may be substituted for a bachelor's degree, for a total of fourteen (14) years of relevant experience without a degree.

Qualifying degrees must be from an accredited college or university in a quantitative discipline, such as Statistics, Mathematics, Operations Research, Engineering, or Computer Science.

Relevant experience must include at least two (2) of the following areas:

• Designing or implementing machine learning algorithms.

• Data mining and advanced analytical algorithm development.

• Advanced statistical analysis.

• Artificial Intelligence (AI) development.

• Software engineering using data analysis tools such as R, Python, SAS, or MATLAB.

Experience supporting production machine learning systems, MLOps practices, scalable analytics, and technical leadership is highly relevant to this position.

Security Clearance and

Additional…