Statistical Data Scientist - Python

ASRC Federal is seeking a Data Scientist to support the DEA's Diversion Division.   Work arrangement: Remote Clearance: Public Trust (must be a US Citizen to meet contract requirements) and successfully complete a government background investigation.   Summary:  The ideal candidate is a highly skilled professional who uses their expertise in data analysis, statistical modeling, and machine learning to extract insights and solve complex problems. Responsibilities: Data Analysis and Modeling:• Collect, clean, and preprocess large datasets from various sources.• Apply statistical techniques and data mining algorithms to analyze data and identify patterns, trends, and relationships.• Develop and implement predictive models, machine learning algorithms, and statistical models to solve business problems and generate actionable insights. Machine Learning and AI:• Design and develop machine learning models and algorithms to solve specific business challenges.• Train, validate, and optimize models using appropriate techniques such as cross-validation and hyperparameter tuning.• Deploy models into production environments and monitor their performance. Data Visualization and Reporting:• Communicate complex data analysis results and insights to non-technical stakeholders through clear and visually appealing data visualizations, reports, and presentations.• Collaborate with cross-functional teams to understand their data needs and provide data-driven recommendations. Data Exploration and Feature Engineering:• Conduct exploratory data analysis to understand the characteristics and quality of the data.• Identify and engineer relevant features from raw data to improve model performance and accuracy. Collaboration and Communication:• Collaborate with data engineers, software developers, and domain experts to gather requirements, define data needs, and implement data-driven solutions.• Communicate findings, methodologies, and insights to both technical and non-technical audiences effectively. Requirements:  Strong knowledge of statistical analysis, machine learning algorithms, and data modeling techniques. Proficiency in programming languages such as Python or R, and experience with data manipulation and analysis libraries (e.g., pandas, NumPy, scikit-learn). Experience with data visualization tools (e.g., Tableau, matplotlib, ggplot) to effectively communicate insights. Familiarity with big data technologies (e.g., Hadoop, Spark) and cloud platforms (e.g., AWS, Azure) is desirable. Strong problem-solving skills, critical thinking, and the ability to work on complex projects independently. Excellent communication and presentation skills to convey complex concepts to both technical and non-technical stakeholders. Education/Experience: Bachelor's or Master's degree in a quantitative field such as Computer Science, Statistics, Mathematics, or related disciplines. A Ph.D. is a plus. (4 years' experience is the equivalent to a bachelor’s degree, 8 years is equivalent to a Master’s)