Data Scientist

We are a world-class team of professionals who deliver next generation technology and products in robotic and autonomous platforms, ground, soldier, and maritime systems in 50+ locations world-wide. Much of our work contributes to innovative research in the fields of sensor science, signal processing, data fusion, artificial intelligence (AI), machine learning (ML), and augmented reality (AR).

QinetiQ US’s dedicated experts in defense, aerospace, security, and related fields all work together to explore new ways of protecting the American Warfighter, Security Forces, and Allies. Being a part of QinetiQ US means being central to the safety and security of the world around us. Partnering with our customers, we help save lives; reduce risks to society; and maintain the global infrastructure on which we all depend.

Why Join QinetiQ US?

If you have the courage to take on a wide variety of complex challenges, then you will experience a unique working environment where innovative teams blend different perspectives, disciplines, and technologies to discover new ways of solving complex problems.  In our diverse and inclusive environment, you can be authentic, feel valued, be respected, and realize your full potential. QinetiQ US will support you with workplace flexibility, a commitment to the health and well-being of you and your family and provide opportunities to work with a purpose. We are committed to supporting your success in both your professional and personal lives.

QinetiQ US seeks a highly experienced and skilled Data Scientist to join our team. As a Data Scientist, you will be responsible for applying advanced analytics techniques, developing predictive models, and providing data-driven insights to support strategic decision-making. Your expertise in data modeling, machine learning, and statistical analysis will be essential in driving innovation and maximizing the value of our data assets. Although we will consider candidates from outside the immediate space, a background in national security is strongly preferred. 

Data Modeling, Algorithm Development, and Analysis:

• Develop and implement sophisticated data models to analyze complex datasets, identify patterns, and extract actionable insights.
• Apply advanced statistical and machine learning techniques to solve business problems, predict trends, and optimize performance.
• Collaborate with stakeholders to understand data requirements and translate them into effective modeling strategies.
• Develop and implement advanced data models, algorithms, and statistical models to solve complex business problems.
• Apply machine learning techniques, such as regression, clustering, classification, and natural language processing, to extract insights from large and diverse datasets.
• Utilize exploratory data analysis to identify patterns, correlations, and trends in data.

Predictive Modeling and Machine Learning:

• Design, develop, and evaluate predictive models to support forecasting, risk assessment, and decision optimization.
• Utilize machine learning algorithms and techniques to develop models for classification, regression, clustering, and anomaly detection.
• Leverage knowledge of a variety of statistical and machine learning techniques and methods to define and develop programming algorithms; train, evaluate, and deploy predictive analytics models that directly inform mission decisions.
• Conduct model performance evaluation, fine-tuning, and optimization to ensure accurate and reliable predictions.

Statistical Analysis and Experimentation:

• Clean, preprocess, and transform raw data to ensure data quality and integrity.
• Develop and apply data preprocessing techniques, including feature engineering, dimensionality reduction, and outlier detection.
• Collaborate with data engineering teams to optimize data pipelines for efficient data ingestion and preprocessing.
• Execute projects including those intended to identify patterns and/or anomalies in large datasets; perform automated text/data classification and categorization as well as entity recognition, resolution and extraction; and named entity matching.
• Create compelling visualizations and reports to effectively communicate complex analytical findings and insights to both technical and non-technical stakeholders.
• Develop interactive dashboards and data visualizations using tools such as Tableau, Power BI, or similar platforms.
• Transform complex analysis results into clear, concise narratives that drive understanding and action.

Data Exploration and Feature Engineering:

• Clean, preprocess, and transform raw data to ensure data quality and integrity.
• Develop and apply data preprocessing techniques, including feature engineering, dimensionality reduction, and outlier detection.
• Collaborate with data engineering teams to optimize data pipelines for efficient data ingestion and preprocessing.
• Explore and analyze large and complex datasets to identify relevant features and variables for modeling and analysis.
• Collaborate with data engineering teams to access and integrate data from various sources, ensuring data quality and consistency.
• Conduct feature engineering, data transformation, and data preprocessing to optimize model performance and accuracy.

Model Deployment and Monitoring:

• Collaborate with engineering teams to deploy models into production systems, ensuring scalability, reliability, and performance.
• Implement monitoring and evaluation frameworks to track model performance over time, identifying and addressing potential issues.
• Clean, preprocess, and transform raw data to ensure data quality and integrity.
• Develop and apply data preprocessing techniques, including feature engineering, dimensionality reduction, and outlier detection.
• Collaborate with data engineering teams to optimize data pipelines for efficient data ingestion and preprocessing.
• Continuously improve and refine models based on feedback, new data, and evolving business requirements.

Cross-functional Collaboration and Leadership:

• Collaborate with cross-functional teams, including data engineers, business stakeholders, and software developers, to drive data science initiatives and ensure successful project outcomes.
• Provide input on platform architecture and development tools.
• Utilize technical knowledge outside of data science to assist in application development, data pipelines, and AWS cloud infrastructure.

• Bachelor's or Master's degree in a quantitative field, such as Data Science, Computer Science, Statistics, or a related discipline.
• Proven experience (5+ years) as a Data Scientist, with a focus on data modeling, predictive analytics, and machine learning.
• Strong expertise in data modeling techniques, statistical analysis, and machine learning algorithms.
• Solid ability with data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience.
• Proficiency in TensorFlow (or PyTorch), Numpy, Pandas, and other standard Data Science/ML libraries.
• Strong problem-solving and analytical thinking abilities, with the ability to work with complex and unstructured datasets.
• Experience with OCR or other computer vision techniques and models plus.
• Experience with NLP, Document Understanding, LLMs, GenAI a plus.
• Experience with AWS and/or Databricks is a plus.
• Familiarity with Microsoft Power Platform, specifically Power Apps, Power BI, Power Automate, and SharePoint is a plus.

Accessibility/Accommodation:

If because of a medical condition or disability you need a reasonable accommodation for any part of the employment process, please send an e-mail to staffing@us.QinetiQ.com or call (540) 658-2720 Opt. 4 and let us know the nature of your request and contact information.

QinetiQ US is an Equal Opportunity/Affirmative Action employer. All Qualified Applicants will receive equal consideration for employment without regard to race, age, color, religion, creed, sex, sexual orientation, gender identity, national origin, disability, or protected Veteran status.