Please submit your CV in English and indicate your level of English proficiency. Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment. What this opportunity involves  While each project involves unique tasks, contributors may:  - Design original computational data science problems that simulate real-world analytical workflows across industries (telecom, finance, government, e-commerce, healthcare) - Create problems requiring Python programming to solve (using Pandas, Numpy, Scipy, Sklearn, Statsmodels, Matplotlib, Seaborn) - Ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks) - Develop problems requiring non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction - Create deterministic problems with reproducible answers: avoid stochastic elements or require fixed random seeds for exact reproducibility - Base problems on real business challenges: customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency - Design end-to-end problems spanning the complete data science pipeline (data ingestion → cleaning → EDA → modeling → validation → deployment considerations) - Incorporate big data processing scenarios requiring scalable computational approaches - Verify solutions using Python with standard data science libraries and statistical methods - Document problem statements clearly with realistic business contexts and provide verified correct answers What we look for This opportunity is a good fit for Data Science specialists with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:   - 5+ years of hands-on data science experience with proven business impact - Portfolio of completed projects and publications showcasing real-world problem-solving - Expert Python programming for data science (pandas, numpy, scipy, scikit-learn, statsmodels) - Expert statistical analysis and machine learning - deep understanding of algorithms, methods, and their practical applications - Expert with SQL and database operations for data manipulation and analysis - Experience with GenAI technologies (LLMs, RAG, prompt engineering, vector databases) - Understanding of MLOps practices and model deployment workflows - Knowledge of modern frameworks (TensorFlow, PyTorch, LangChain) - Strong written English (C1+). How it works  Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Project time expectations  For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active.  Compensation On this project, contributors can earn up to $90 per hour equivalent, depending on their level and pace of contribution. Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.

Please submit your CV in English and indicate your level of English proficiency. Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment. What this opportunity involves  While each project involves unique tasks, contributors may:  - Design original computational data science problems that simulate real-world analytical workflows across industries (telecom, finance, government, e-commerce, healthcare) - Create problems requiring Python programming to solve (using Pandas, Numpy, Scipy, Sklearn, Statsmodels, Matplotlib, Seaborn) - Ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks) - Develop problems requiring non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction - Create deterministic problems with reproducible answers: avoid stochastic elements or require fixed random seeds for exact reproducibility - Base problems on real business challenges: customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency - Design end-to-end problems spanning the complete data science pipeline (data ingestion → cleaning → EDA → modeling → validation → deployment considerations) - Incorporate big data processing scenarios requiring scalable computational approaches - Verify solutions using Python with standard data science libraries and statistical methods - Document problem statements clearly with realistic business contexts and provide verified correct answers What we look for This opportunity is a good fit for Data Science specialists with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:   - 5+ years of hands-on data science experience with proven business impact - Portfolio of completed projects and publications showcasing real-world problem-solving - Expert Python programming for data science (pandas, numpy, scipy, scikit-learn, statsmodels) - Expert statistical analysis and machine learning - deep understanding of algorithms, methods, and their practical applications - Expert with SQL and database operations for data manipulation and analysis - Experience with GenAI technologies (LLMs, RAG, prompt engineering, vector databases) - Understanding of MLOps practices and model deployment workflows - Knowledge of modern frameworks (TensorFlow, PyTorch, LangChain) - Strong written English (C1+). How it works  Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Project time expectations  For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active.  Compensation On this project, contributors can earn up to $90 per hour equivalent, depending on their level and pace of contribution. Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.

Please submit your CV in English and indicate your level of English proficiency. Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment. What this opportunity involves  While each project involves unique tasks, contributors may:  - Design original computational data science problems that simulate real-world analytical workflows across industries (telecom, finance, government, e-commerce, healthcare) - Create problems requiring Python programming to solve (using Pandas, Numpy, Scipy, Sklearn, Statsmodels, Matplotlib, Seaborn) - Ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks) - Develop problems requiring non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction - Create deterministic problems with reproducible answers: avoid stochastic elements or require fixed random seeds for exact reproducibility - Base problems on real business challenges: customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency - Design end-to-end problems spanning the complete data science pipeline (data ingestion → cleaning → EDA → modeling → validation → deployment considerations) - Incorporate big data processing scenarios requiring scalable computational approaches - Verify solutions using Python with standard data science libraries and statistical methods - Document problem statements clearly with realistic business contexts and provide verified correct answers What we look for This opportunity is a good fit for Data Science specialists with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:   - 5+ years of hands-on data science experience with proven business impact - Portfolio of completed projects and publications showcasing real-world problem-solving - Expert Python programming for data science (pandas, numpy, scipy, scikit-learn, statsmodels) - Expert statistical analysis and machine learning - deep understanding of algorithms, methods, and their practical applications - Expert with SQL and database operations for data manipulation and analysis - Experience with GenAI technologies (LLMs, RAG, prompt engineering, vector databases) - Understanding of MLOps practices and model deployment workflows - Knowledge of modern frameworks (TensorFlow, PyTorch, LangChain) - Strong written English (C1+). How it works  Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Project time expectations  For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active.  Compensation On this project, contributors can earn up to $90 per hour equivalent, depending on their level and pace of contribution. Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.

Role Description At Zebra, we are a community of innovators who come together to create new ways of working. United by curiosity and a culture of caring, we develop smart solutions that anticipate our customer’s and partner’s needs and solve their challenges. Being a part of Zebra Nation means you are seen, heard, valued, and respected. Drawing from our unique perspectives, we collaborate to deliver on our purpose. Here you are a part of a team pushing boundaries today to redefine the work of tomorrow for organizations, their employees, and those they serve. You'll have opportunities to learn and lead in a forward-thinking environment, defining your path to a fulfilling career while channeling your skills toward causes you care about – locally and globally. Come make an impact every day at Zebra. - Design, optimize, and maintain scalable ETL pipelines using PySpark, and Databricks on cloud platforms (Azure/GCP). - Develop automated data validation process to proactively perform data quality checks. - Employ key Databricks modules (DeltaLake, Unity Catalog, MLFlow) to facilitate creating and scheduling jobs on Databricks. - Optimize the allocation of cloud resources to manage and control cloud cost. - Employ GitHub code management repositories and ensure that best practices are being implemented. - Build and tune ML/AI and optimization models, identify improvement opportunities, and perform experiments to demonstrate incremental value. - Have frequent conversations with Business Stakeholders to understand their requirements and concerns. Explain data deficiencies, model performance/root cause analysis, and model explainability. - Follow best practices in Data Architecture, Coding, and Project Management operations. - Collaborate with cross-functional teams, such as Customers’ Stakeholders, Engagement Managers, Data Ops/Job Monitoring, Product Management & Software Engineering. - Expand the use of analytics, ML/AI, mathematical optimization, Gen-AI/LLMs and Agentic-AI in the context of Retail/CPG business use cases such as: - Anomaly detection - Demand forecasting - Price elasticity modeling - Promotions features & strategy simulation - Product cannibalization and halo modeling - Markdown optimization - Product allocation - Reorder/replenishment - Size and pack optimization - Workforce scheduling & task optimization Qualifications - Minimum Education: Master’s degree in engineering, computer science, data science, operations research, statistics, mathematics, quantitative sciences or relevant work experience. - Minimum Work Experience: 4+ years of experience in Data Science/Data Engineering with emphasis on the full lifecycle of Data Science-ML/AI projects. - Experience in Python/PySpark, SQL, and relational or NoSQL databases, and cloud resource management. - Experience working with AWS, Azure, or GCP cloud environments. - Experience implementing advanced analytics, ML/AI algorithms (such as statistical time series: ESMs, ARIMA; machine learning: Random Forests, GBMs; Neural Networks: TiDE & DenseNet, foundational models) and mathematical (constrained linear/non-linear and network) optimization models. - Proven experience building end-to-end production grade Data & ML/AI pipelines using PySpark, Python (Pandas/NumPy) and SQL. - Experience working with Git (or similar code management repositories) as a collaboration tool. - Experience with orchestration tools like Databricks, Airflow (or similar tools like Snowflake, Dagster, etc.). - Working knowledge of GenAI/LLMs, Agentic-AI and related frameworks (e.g. LangChain) will be a plus. - Understanding of Retail/CPG industry business challenges with an emphasis on Supply Chain, Pricing and Workforce optimization applications is preferred. - Excellent verbal and written communication skills, especially as it relates to technical communications. Ability to present technical analysis to business stakeholders. - Demonstrated ability to learn new technologies quickly and independently. - Ability to work independently with minimal supervision and achieve stretch goals in a very innovative and fast-paced environment. Requirements - Licenses/Certifications: N/A - Equivalencies: Relevant work experience may be substituted for a degree. Benefits - To protect candidates from falling victim to online fraudulent activity involving fake job postings and employment offers, please be aware our recruiters will always connect with you via @zebra.com email accounts. - Applications are only accepted through our applicant tracking system and only accept personal identifying information through that system. - Our Talent Acquisition team will not ask for you to provide personal identifying information via e-mail or outside of the system. - If you are a victim of identity theft contact your local police department.