1

Weekend Data Science Jobs in Michigan (NOW HIRING)

Your mission is to build and scale trusted data science products that power marketing performance measurement while promoting data science best practices, actionable recommendations and a high bar ...

Principal Data Scientist (US)

Southfield, MI · On-site +1

$148K - $223K/yr

Leads authority on Data Science concepts, principals, practices and standards * Thought leader on IT risk-based approaches to Data Science * Sets the direction and oversees the work of other Data ...

Leads authority on Data Science concepts, principals, practices and standards * Thought leader on IT risk-based approaches to Data Science * Sets the direction and oversees the work of other Data ...

Translate complex business questions and challenges into well-defined data science problems and analytical tasks. * Develop, train, and evaluate statistical and machine learning models to address ...

Your mission is to build and scale trusted data science products that power marketing performance measurement while promoting data science best practices, actionable recommendations and a high bar ...

Data Scientist

Plymouth, MI · On-site

$90 - $130/hr

Bachelor's degree in Data Science, Computer Science, Information Systems, Finance, Quantitative Methods, Statistics, Engineering, or a related discipline; an equivalent combination of education and ...

Data Scientist

Sterling Heights, MI · On-site

$50 - $58/hr

Bachelor's Degree in Data Management, Data Science, Math, Statistics or equivalent Education Preferred: Master's Degree in mathematics, statistics, business, engineering, physical/applied sciences ...

Bachelor's degree in a quantitative field (e.g., Computer Science, AI, Statistics, Mathematics, or ... Experience with data augmentation and efficient loading techniques. * Expert proficiency in Python ...

Showing results 21-40

Weekend Data Science information

What is a weekend data science?

A Weekend Data Science job typically refers to a part-time or contract-based data science position where the primary work hours are on weekends. These roles are ideal for students, professionals seeking extra income, or those looking to gain experience in the data science field without committing to a full-time weekday schedule. Weekend data scientists analyze data, build models, and generate insights just like full-time data scientists but with flexible or reduced hours that fit around a weekend schedule.

What skills and qualifications are needed to thrive as a weekend data scientist?

To thrive as a Weekend Data Scientist, you need strong analytical skills, proficiency in statistics, and expertise in programming languages such as Python or R, often supported by a degree in a quantitative field. Familiarity with data analysis tools like SQL, machine learning libraries (e.g., scikit-learn, TensorFlow), and data visualization platforms (e.g., Tableau) is typically required. Excellent time management, problem-solving ability, and effective communication are crucial soft skills for delivering insights on tight weekend deadlines. These skills ensure that data-driven decisions can be made efficiently and accurately, even within limited time frames.

What challenges do data scientists working on weekends face, and how can they be managed?

Data scientists working weekend shifts often encounter challenges such as limited access to colleagues for collaboration or support, since many team members may not be available outside standard business hours. Additionally, urgent issues or data anomalies may require quick, independent problem-solving. Proactive communication with weekday teams, thorough documentation, and setting up clear protocols for handoffs can help manage these challenges and ensure smooth workflow continuity.

What is the difference between Weekend Data Science vs Part-Time Data Analyst?

AspectWeekend Data SciencePart-Time Data Analyst
CredentialsTypically requires a degree in data science, statistics, or related fieldOften requires a degree or relevant experience in data analysis or related fields
Work EnvironmentProject-based, flexible hours, often remote or on-site during weekendsFlexible hours, may be remote or on-site, often with less technical complexity
Industry UsageUsed in tech, finance, healthcare, and startups for specialized projectsCommon in retail, marketing, and small businesses for routine data tasks

Weekend Data Science roles focus on complex data projects requiring advanced skills, often during weekends, while Part-Time Data Analysts handle routine data tasks with less technical depth, offering flexible schedules. Both roles serve different needs but share a focus on data work outside standard hours.

Do weekend data scientists work on weekends?

Weekend data scientists may work on weekends depending on project deadlines, company policies, or client needs. Typically, data science roles involve regular weekday hours, but some positions require weekend work, especially in roles with flexible or project-based schedules. It is important to clarify work hours during the hiring process or in job descriptions.

What are the most commonly searched types of Data Science jobs in Michigan?

The most popular types of Data Science jobs in Michigan are:

What cities in Michigan are hiring for Weekend Data Science jobs?

Cities in Michigan with the most Weekend Data Science job openings:

Infographic showing various Weekend Data Science job openings in Michigan as of August 2026, with employment types broken down into 85% Full Time, and 15% Part Time. Highlights an 90% In-person, and 10% Remote job distribution.

Data Science and Machine Learning Senior Associate

KYYBA Inc

Dearborn, MI • On-site

Full-time

Re-posted 12 days ago


Job description

Job Summary:
KYYBA Inc is a company seeking a Data Science and Machine Learning Senior Associate to leverage data science methodologies for predicting and extracting meaningful trends from raw data. The role involves designing and implementing data analysis and machine learning models to support data-driven decision-making.
Responsibilities:
• Understand business requirements and analyze datasets to determine suitable approaches to meet analytic business needs and support data-driven decision-making
• Design and implement data analysis and ML models, hypotheses, algorithms and experiments to support data driven decision-making
• Apply various analytics techniques like data mining, predictive modeling, prescriptive modeling, math, statistics, advanced analytics, machine learning models and algorithms, etc.; to analyze data and uncover meaningful patterns, relationships, and trends
• Design efficient data loading, data augmentation and data analysis techniques to enhance the accuracy and robustness of data science and machine learning models, including scalable models suitable for automation
• Research, study and stay updated in the domain of data science, machine learning, analytics tools and techniques etc.; and continuously identify avenues for enhancing analysis efficiency, accuracy and robustness
• Model Development: Design, develop, and deploy high-performance machine learning models (supervised, unsupervised, and reinforcement learning) to address business needs such as churn prediction, recommendation engines, or demand forecasting
• Experimental Design: Lead the design and analysis of large-scale experiments (A/B testing, multivariate testing) to validate hypotheses and measure the impact of product changes
• Feature Engineering: Architect and implement robust data pipelines and feature engineering processes to improve model accuracy and scalability
• Algorithm Optimization: Evaluate and refine existing algorithms to improve computational efficiency and predictive power
• Stakeholder Influence: Act as a strategic advisor to leadership, translating complex algorithmic outcomes into business-centric narratives that drive ROI
• Technical Leadership: Mentor junior data scientists and contribute to the team’s internal library of best practices, code standards, and research methodologies
• Collaboration with Engineering: Partner with ML Engineers and DevOps to integrate models into production systems, ensuring reliability and monitoring model drift.
Qualifications:
Required:
• 5+ years of experience in a Data Science role, with a proven track record of delivering models that impact business outcomes.
• Expert proficiency in Python (specifically libraries like Pandas, NumPy, Scikit-learn, SciPy) or R.
• Deep understanding of a broad range of ML techniques, including Gradient Boosting (XGBoost/LightGBM), Random Forests, GLMs, and Clustering.
• Ability to manipulate and extract data from complex, multi-terabyte distributed databases.
• Strong foundation in linear algebra, calculus, and advanced statistical inference.
• Experience with version control (Git) and writing clean, modular, and maintainable code.
• Master's Degree
Company:
Kyyba, Inc. Founded in 1998, the company is headquartered in Farmington Hills, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

KYYBA logo

About KYYBA

Sourced by ZipRecruiter

About Kyyba: Founded in 1998 and headquartered in Farmington Hills, MI, Kyyba has a global presence delivering high-quality resources and top-notch recruiting services, enabling businesses to effectively respond to organizational changes and technological advances. At Kyyba, the overall well-being of our employees and their families is important to us. We are proud of our work culture which embodies our core values; incorporating value, passion, excellence, empowerment, and happiness, creates a vibrant and productive atmosphere. We empower our employees with the resources, incentives, and flexibility that they need to support a healthy, balanced, and fulfilling career by providing many valuable benefits and a balanced compensation structure combined with career development.

Industry

Recruiting and staffing services

Company size

501 - 1,000 Employees

Headquarters location

Farmington Hills, MI, US

Year founded

1998

Social media