Postdoctorate Scientist jobs in Warren, MI

Preferred Qualifications:

• Master's degree or doctorate in statistics, data science, mathematics, or related applied science field, or equivalent experience
• Proficiency in natural language processing (NLP) and machine learning techniques, with a track record of implementing Generative AI solutions in real-world applications
• Extensive experience with statistical tools and packages such as Python, R, SAS and methods to incorporate statistical results into analyses
• Strong proficiency in Python and at least one strongly typed language (e.g., C#, C , Java, etc.)
• Proficient in standard querying with SQL
• Advanced knowledge of statistical methods, particularly in modeling and business analytics
• Ability to manipulate and analyze complex, high-volume, high-dimensionality data including structured and non-structured data from various sources
• Strong communication skills for conveying complex quantitative analyses clearly, precisely, and actionable
• Minimum of 2 years of experience working with large datasets in AWS, Azure, or GCP environments
• Minimum of 2 years of experience with supervised models (e.g., regression, neural networks) and unsupervised techniques (e.g. clustering, SVMs)
• Proven track record as a lead resource for the business
• Strong written and verbal communication skills

Job Summary:

As a Data Scientist, you'll collaborate with business stakeholders and technology teams to provide in-depth analysis and guidance on critical business opportunities across various areas within the company. Leveraging expertise in applied statistics, machine learning, AI, data mining, modeling and forecasting, you'll ensure that results are accurate, clear and actionable for business leaders. You'll be detail-oriented and keenly aware of the impact of your analysis and solutions throughout the company, focusing on finding innovative solutions for a range of business challenges.

Responsibilities:

• Collaborate with teams across the company to identify and solve business challenges using structured, semi-structured, and unstructured data
• Drive the collection of new data and refinement of existing data sources for statistical analysis, AI and ML modeling
• Develop and implement cutting-edge Generative AI models to enhance communication within the organization
• Collaborate with cross-functional teams to integrate AI-driven solutions into existing workflows
• Clean and prepare data for analysis and modeling
• Perform statistical summaries and tests for relevant business questions
• Develop predictive statistical, behavioral, or other models using supervised and unsupervised machine learning techniques
• Translate business opportunities into data-driven machine learning solutions to optimize business processes
• Deploy models into production systems for business use
• Conduct statistical testing to improve the predictability of deployed models
• Summarize conclusions and solutions for presentation
• Cultivate strong relationships with subject matter experts
• Develop best practices for experimentation and development