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

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description This role provides a unique opportunity to contribute to early-stage immuno-oncology clinical programs by leading the development and execution of statistical programming and analyses. You will ensure submission-ready study data packages, maintain compliance with CDISC standards and FDA guidance, and support internal and external stakeholders across biostatistics, clinical operations, and regulatory teams. The position offers hands-on technical work with SAS, R, Python, and Unix, while influencing program quality, efficiency, and innovation. You will operate in a collaborative, fast-paced environment, managing complex datasets, generating TFLs, and creating SDTM/ADaM outputs for regulatory submissions and publications. This contract role allows for remote work within the United States, providing both flexibility and the chance to make a tangible impact on the clinical development pipeline. - Develop specifications and execute programs for internal data reviews, publications, exploratory, post-hoc, and regulatory analyses - Ensure study data packages comply with CDISC standards (SDTM, ADaM) and health authority reporting requirements - Collaborate with clinical data management to review case report forms, database, and data transfer specifications - Create and review SDTM/ADaM mapping specifications, reviewer’s guides, and define.xml files - Generate or validate TFLs to support statistical analyses for clinical studies, regulatory submissions, and publications - Prioritize analytical deliverables strategically, including study-level or integrated analyses for safety signal detection or business needs - Manage timely and quality delivery of CRO-generated analysis results Qualifications - Bachelor’s or Master’s degree in Statistics, Mathematics, Epidemiology, Computer Science, Bioinformatics, or related field - 8+ years of programming experience in biotechnology or pharmaceutical clinical trial environments - Expert-level skills in SAS programming, with strong experience in R, Python, Unix scripts, and macro development - Solid understanding of CDISC standards (SDTM, ADaM), controlled terminology, and regulatory submission requirements - Ability to translate complex clinical and statistical problems into clear, functional code and tools - Experience with software development and testing, project management, and programming best practices - Knowledge of ICH-GCP, 21 CFR Part 11, and relevant clinical R&D concepts - Excellent written and verbal communication skills, with the ability to manage priorities and collaborate effectively Benefits - Competitive contract compensation - Flexible remote work within the United States - Exposure to cutting-edge immuno-oncology research and clinical development - Opportunity to work collaboratively with a growing Biometrics team - Professional growth through experience with regulatory submissions and complex statistical programming