• Lead data exploration and analysis on large scale financial crime datasets — including sanctions, PEP (Politically Exposed Persons), and adverse media data — to uncover patterns, identify false positives/negatives, and drive feature improvements. • Develop and evaluate agents and rule-based models by running experiments, validating hypotheses, and fine-tuning thresholds to improve alert efficiency. • Build and deliver production-ready API integrations — coordinating with software engineers and product teams to ensure components are properly integrated, tested, and merged. • Conduct customer-focused data studies across multiple enterprise clients (e.g., financial institutions) to benchmark model performance, assess data quality, and propose data driven solutions to reduce investigation loads. • Prototype and iterate quickly — using PySpark, Jupyter notebooks, and Python to explore data, build reproducible pipelines, and generate insights that inform product decisions. • Investigate and resolve product issues in collaboration with engineering and product teams. • Contribute to R&D on emerging techniques — including graph-based approaches (GNNs, graph embeddings) for transaction monitoring, LLM-based feature exploration, and RAG-based models. • Communicate findings clearly through well-organized Jupyter notebooks, internal documentation, and stakeholder presentations, translating complex analytical results into actionable business insights.

• Serve as a senior technical authority for LiDAR and geospatial data workflows supporting wildfire mitigation and hazard patrol programs. • Assume immediate ownership of existing Python- and SQL-based workflows used to generate circuit maps and operational deliverables. • Oversee ingestion of LiDAR data received from vendors and contractors, ensuring accuracy, completeness, and adherence to program standards. • Design, implement, and maintain QA/QC processes for LiDAR and derived datasets, with a focus on automation, repeatability, and auditability. • Support and enhance workflows that load processed data into SQL Server environments and feed downstream analytics. • Enable and maintain reporting pipelines from SQL Server into Power BI for operational, compliance, and stakeholder reporting. • Support the creation and annual submission of deliverables, including digital maps and supporting datasets. • Lead and actively contribute to cloud migration efforts, moving critical applications and workflows from local SME machines to Azure and/or AWS. • Apply practical cloud expertise to reduce technical debt and accelerate modernization efforts that have previously stalled. • Develop, update, and maintain process documentation and job aids to support knowledge transfer and reduce reliance on single individuals. • Ensure workflows, applications, and processes are understandable and supportable by client stakeholders and internal teams. • Actively collaborate with multiple vendors, contractors, remote sensing teams, field operations, and internal stakeholders. • Serve as a trusted partner to client stakeholders, working through complex dependencies and shared ownership models. • Communicate clearly, professionally, and proactively regarding progress, risks, technical decisions, and recommendations. • Identify opportunities for process improvement, efficiency gains, and automation across the data lifecycle.

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 chemistry problems that simulate real chemistry research workflows; - Create problems requiring Python programming to solve (using numpy, scipy, chemical libraries); - Ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks); - Develop problems requiring non-trivial reasoning chains in physical chemistry, quantum chemistry, and molecular modeling; - Base problems on real research challenges or practical applications from chemistry practice; - Verify solutions using Python with standard computational chemistry approaches; - Document problem statements clearly and provide verified correct answers. What we look for This opportunity is a good fit for chemistry specialists with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:   - Degree in Chemistry or related fields; - Python proficiency for numerical validation. MATLAB, R, C, SQL, Numpy, Pandas, SciPy, domain-specific libraries, Stata or knowledge of any programming language can be equivalent; - 2+ years of professional experience: applied, research, or teaching experience is applicable; - Experience with numerical methods in chemistry; - Familiarity with computational chemistry concepts and methods; - 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 $29 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 chemistry problems that simulate real chemistry research workflows; - Create problems requiring Python programming to solve (using numpy, scipy, chemical libraries); - Ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks); - Develop problems requiring non-trivial reasoning chains in physical chemistry, quantum chemistry, and molecular modeling; - Base problems on real research challenges or practical applications from chemistry practice; - Verify solutions using Python with standard computational chemistry approaches; - Document problem statements clearly and provide verified correct answers. What we look for This opportunity is a good fit for chemistry specialists with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:   - Degree in Chemistry or related fields; - Python proficiency for numerical validation. MATLAB, R, C, SQL, Numpy, Pandas, SciPy, domain-specific libraries, Stata or knowledge of any programming language can be equivalent; - 2+ years of professional experience: applied, research, or teaching experience is applicable; - Experience with numerical methods in chemistry; - Familiarity with computational chemistry concepts and methods; - 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 $45 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.