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 rigorous statistics problems reflecting professional practice; - Evaluate AI solutions for correctness, assumptions, and constraints; - Validate calculations or simulations using Python (NumPy, Pandas, SciPy, Statsmodels, and Scikit-learn); - Improve AI reasoning to align with industry-standard logic; - Apply structured scoring criteria to multi-step problems. What we look for This opportunity is a good fit for statisticians with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:  - Degree in Statistics or related fields, e.g. Probability Theory, Mathematical Statistics, Applied Statistics, etc. - 3+ years of professional mathematics experience - Strong written English (C1/C2) - Strong Python proficiency for numerical validation - Stable internet connection  Professional certifications (e.g., PStat, CAP, SAS Certifications) and experience in international or applied projects are an advantage. 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. Payment - Paid contributions, with rates up to $73/hour*  - Fixed project rate or individual rates, depending on the project - Some projects include incentive payments *Note: Rates vary based on expertise, skills assessment, location, project needs, and other factors. Higher rates may be offered to highly specialized experts. Lower rates may apply during onboarding or non-core project phases. Payment details are shared per project.

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 rigorous statistics problems reflecting professional practice; - Evaluate AI solutions for correctness, assumptions, and constraints; - Validate calculations or simulations using Python (NumPy, Pandas, SciPy, Statsmodels, and Scikit-learn); - Improve AI reasoning to align with industry-standard logic; - Apply structured scoring criteria to multi-step problems. What we look for This opportunity is a good fit for statisticians with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:  - Degree in Statistics or related fields, e.g. Probability Theory, Mathematical Statistics, Applied Statistics, etc. - 3+ years of professional mathematics experience - Strong written English (C1/C2) - Strong Python proficiency for numerical validation - Stable internet connection  Professional certifications (e.g., PStat, CAP, SAS Certifications) and experience in international or applied projects are an advantage. 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. Payment - Paid contributions, with rates up to $73/hour*  - Fixed project rate or individual rates, depending on the project - Some projects include incentive payments *Note: Rates vary based on expertise, skills assessment, location, project needs, and other factors. Higher rates may be offered to highly specialized experts. Lower rates may apply during onboarding or non-core project phases. Payment details are shared per project.

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 rigorous statistics problems reflecting professional practice; - Evaluate AI solutions for correctness, assumptions, and constraints; - Validate calculations or simulations using Python (NumPy, Pandas, SciPy, Statsmodels, and Scikit-learn); - Improve AI reasoning to align with industry-standard logic; - Apply structured scoring criteria to multi-step problems. What we look for This opportunity is a good fit for statisticians with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:  - Degree in Statistics or related fields, e.g. Probability Theory, Mathematical Statistics, Applied Statistics, etc. - 3+ years of professional mathematics experience - Strong written English (C1/C2) - Strong Python proficiency for numerical validation - Stable internet connection  Professional certifications (e.g., PStat, CAP, SAS Certifications) and experience in international or applied projects are an advantage. 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. Payment - Paid contributions, with rates up to $73/hour*  - Fixed project rate or individual rates, depending on the project - Some projects include incentive payments *Note: Rates vary based on expertise, skills assessment, location, project needs, and other factors. Higher rates may be offered to highly specialized experts. Lower rates may apply during onboarding or non-core project phases. Payment details are shared per project.

• The Manager, Predictive Analytics and Data Science is responsible for serving as the leader, subject matter expert, teacher, and mentor of the Predictive Analytics and Data Science team. • Lead a team of data scientists and analysts to develop and execute predictive analytics projects and initiatives • Collaborate with cross-functional teams, including IT, healthcare operations, finance, and clinical teams, to identify opportunities for data-driven solutions and predictive modeling applications • Develop and implement predictive models, algorithms, and statistical techniques to extract insights from large and complex healthcare datasets • Utilize machine learning algorithms to identify patterns, trends, and opportunities for improving operational efficiency, cost containment, and patient care • Utilize Natural Language Processing (NLP) algorithms and techniques to process and analyze unstructured healthcare data, such as clinical notes, patient feedback, and medical literature, to extract meaningful insights • Conduct rigorous data analysis, including data cleansing, feature engineering, and exploratory data analysis, to derive meaningful insights and actionable recommendations • Stay abreast of emerging trends, tools, and techniques in predictive analytics, data science, and healthcare informatics to drive innovation and continuous improvement • Collaborate with stakeholders to define key performance indicators (KPIs), develop metrics, and create dashboards and reports that effectively communicate insights and support decision-making • Provide strategic guidance and recommendations to senior leadership based on data analysis and predictive modeling results • Mentor and develop team members, fostering a collaborative and innovative work environment • Ensure compliance with data privacy and security regulations and maintain the highest standards of data integrity