Senior Specialist - Data Science

Ally•Detroit, MI

1d•$85,000 - $150,000

About The Position

The Senior Specialist - Data Science applies advanced data science, machine learning, and Generative AI techniques to solve complex business problems and enable AI-powered capabilities across the enterprise. The role transforms complex data into meaningful insights and develops, evaluates, and deploys analytical and AI solutions that support Ally Vision. The position combines data analysis, statistical methods, machine learning, and programming expertise with experience applying Generative AI and large language models to business problems. The specialist partners with business and technology stakeholders to identify opportunities, translate business needs into analytical solutions, and ensure models and AI applications are accurate, scalable, responsible, and effective. The role also provides technical guidance and mentorship to less-experienced data scientists.

Requirements

At least 3 years of relevant experience.
Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or equivalent.
Strong analytical, problem-solving, and communication skills, including the ability to translate complex analytical findings into business insights and provide technical guidance to data scientists or analysts.

Nice To Haves

At least 3 years of experience in data science, machine learning, advanced analytics, or a related quantitative field, with advanced Python programming skills and experience with common data science and machine learning libraries.
Master's degree in a relevant quantitative discipline.
Experience with SQL and large, complex datasets, including preparing, transforming, and analyzing structured and unstructured data.
Experience applying statistical analysis, predictive modeling, machine learning, feature engineering, experimentation, and model evaluation to complex business problems.
Hands-on experience developing or evaluating Generative AI and LLM-based solutions, including RAG, embeddings, semantic search, prompt engineering, and/or agentic AI.
Experience with scikit-learn, PyTorch, or TensorFlow and integrating internally hosted or third-party foundation models.
Experience working in AWS, Azure, or GCP, deploying analytical, machine learning, or AI solutions, and applying MLOps/LLMOps practices such as monitoring, versioning, reproducibility, and observability.
Knowledge of Responsible AI, model governance, data privacy, security, and appropriate handling of sensitive data and PII.

Responsibilities

Analyze large and complex datasets using statistical, machine learning, and advanced analytical techniques to identify patterns, generate insights, and solve business problems.
Design, develop, evaluate, and optimize machine learning models, including feature engineering, model validation, performance testing, and ongoing monitoring.
Develop and evaluate Generative AI solutions using foundation models, including prompt engineering, retrieval-augmented generation (RAG), embeddings, semantic search, and agentic AI techniques where appropriate.
Develop evaluation frameworks and experimentation approaches to measure model quality, accuracy, relevance, safety, and business impact; evaluate alternatives and communicate technical trade-offs and recommendations.
Acquire, cleanse, transform, and integrate structured and unstructured data from multiple sources for analytical modeling and AI solutions.
Partner with engineering teams to deploy data science and AI solutions into production and support model lifecycle management, monitoring, reproducibility, performance optimization, and scalability.
Collaborate with business and technology partners to define problems, identify appropriate data science approaches, translate analytical findings into actionable recommendations, and communicate complex concepts to technical and non-technical audiences.
Apply controls and governance practices related to model risk, explainability, data privacy, security, bias, and responsible AI use.
Mentor less-experienced data scientists, contribute to technical design and methodology decisions, and help establish reusable data science, machine learning, and AI best practices.

Benefits

Market-competitive base pay and performance-based annual incentive plan.
Paid time off starting at 20 days, 11 paid holidays, and 8 hours of annual volunteer time off; amounts and program details vary by start date, employment status, and management level.
401(k) retirement savings plan with matching and company contributions.
Student loan pay-down and 529 educational savings assistance programs.
Tuition reimbursement.
Employee stock purchase plan.
Financial learning center and financial coach access.
Medical, dental, and vision insurance options.
Employee, spouse, and child life insurance.
Short- and long-term disability insurance.
Health Savings Account with employer contributions and Healthcare FSA.
Critical illness, accident, and hospital indemnity insurance.
Total well-being program for physical, social, emotional, and financial well-being.
Adoption, surrogacy, and fertility assistance.
Paid parental and caregiver leave.
Dependent Day Care FSA, backup child and adult/elder care days, and childcare discounts.
Employee Assistance Program.
Subsidized and discounted Weight Watchers program and other employee discount programs.
Depending on the role, potential travel allowances, relocation assistance, signing bonus, and/or equity.