Role Description We are sharing a specialised full-time consulting opportunity for experienced data scientists and quantitative analysts with strong expertise in statistical analysis, data cleaning, method comparison, reproducible research, and evidence-based reporting. This role supports the development of advanced agentic evaluation benchmarks for frontier AI models. Selected professionals will create realistic data-analysis challenges, develop reproducible reference notebooks, evaluate model-generated analyses, and identify where statistical reasoning, interpretation, or reporting falls short of professional standards. Key Responsibilities - Data Analysis Task Design - Create realistic analytical tasks based on professional data science and quantitative research workflows. - Develop assignments involving messy data, anomaly detection, correlation analysis, hypothesis testing, and method comparison. - Design complex, multi-step problems requiring statistical judgment and careful interpretation. - Ensure tasks include realistic constraints, datasets, assumptions, and decision-making objectives. - Reproducible Notebook Development - Complete reference analyses using Jupyter Notebook or Google Colab. - Build clear and reproducible workflows using Python, pandas, NumPy, and related libraries. - Document data-cleaning decisions, calculations, statistical methods, and analytical conclusions. - Validate intermediate results, spot checks, visualisations, and final recommendations. - Statistical Method Comparison - Design fair comparisons between analytical models, algorithms, or statistical approaches. - Evaluate performance using appropriate metrics, manual checks, and sensitivity analyses. - Identify methodological trade-offs, limitations, and sources of uncertainty. - Produce recommendations supported by transparent quantitative evidence. - AI Model Evaluation - Review model-generated analyses for statistical accuracy, methodological rigour, and sound interpretation. - Verify whether calculations, correlations, hypotheses, and conclusions are supported by the data. - Identify coding errors, unsupported assumptions, misleading summaries, and analytical shortcuts. - Explain where and why model outputs fail to meet professional data-analysis standards. - Research Collaboration - Work closely with researchers, task authors, and fellow quantitative specialists. - Compare evaluation decisions to maintain consistent benchmark standards. - Refine tasks, reference notebooks, and grading criteria based on testing outcomes. - Document recurring model weaknesses and opportunities for stronger evaluation coverage. Qualifications - At least 1 year of experience in data science, quantitative analysis, research engineering, or another research-intensive analytical role. - Deep hands-on experience with data cleaning, statistical correlation, hypothesis testing, and interpretation. - Strong proficiency in Python, including pandas, NumPy, or comparable analytical libraries. - Experience using Jupyter Notebook or Google Colab for analysis and reporting. - Working familiarity with Git and reproducible analytical workflows. - Ability to communicate complex quantitative findings clearly to technical and non-technical decision-makers. - Strong attention to detail and confidence working through ambiguous, open-ended problems. - Reliable availability for approximately 35 hours per week. Educational Background - A master's degree or PhD in statistics, data science, mathematics, economics, computer science, engineering, or another quantitative discipline is highly relevant. - Equivalent practical experience in a research-heavy analytical field may also be considered. - Academic or professional research involving statistical modelling, experimentation, or large-scale data analysis may strengthen an application. - Publications, technical reports, open-source work, or impactful analytical projects may also be valuable. Nice to Have - Experience in AI training, model evaluation, or benchmark development. - Background authoring analytical tasks, reference solutions, or grading rubrics. - Familiarity with anomaly detection, experimental design, or comparative model evaluation. - Experience conducting manual spot checks and validating automated analyses. - Knowledge of statistical modelling, machine learning, or scientific computing. - Familiarity with agentic AI systems and multi-step model evaluations. - Experience reviewing notebooks, code, or analyses prepared by other professionals. - Strong ability to identify subtle statistical errors and unsupported conclusions. Why This Opportunity - Apply advanced data science and quantitative analysis expertise to frontier AI evaluation. - Design realistic tasks grounded in professional analytical workflows. - Help improve how AI systems reason through statistics, data quality, and method comparison. - Work across Python, reproducible notebooks, model evaluation, and evidence-based reporting. - Collaborate closely with researchers and other quantitative specialists. - Participate in a structured full-time remote role with competitive hourly compensation. Contract Details - Full-time W-2 contingent employment opportunity. - Fully remote within the United States. - Expected commitment of approximately 35 hours per week. - Competitive rates between $55–$85 per hour depending on expertise and project scope. - Individual tasks may require one to two days of focused analysis and implementation. - Work may include task design, data cleaning, statistical analysis, notebook development, AI output evaluation, and technical reporting. - Engagement scope and duration may evolve according to project requirements and performance. About the Platform This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.

• Act as the single point of contact to oversee contract execution and ensure alignment with strategic goals. • Provides contract coordination, performance accountability, and strategic stakeholder engagement. • Operates with full authority to act for the Contractor on all contract matters relating to daily operations. • Serve as the senior technical authority across all development, architecture, integration, and modernization activities. • Define the program’s technical strategy, architecture vision, engineering standards, and modernization roadmap. • Ensure technical alignment with enterprise, security, and compliance standards. • Manage internal and external communications strategy, reporting, and coordination. • Responsible for the overall performance of the call order such as formulating work standards, assigning Contractor schedules and resources, reviewing performance, cost, and budget information; supervising Contractor personnel; and communicating policies, purposes, and goals of the organization to subordinates. • Ensure desired results by determining and implementing objectives and allocating appropriate resources. • Manage the development, operations, and implementation tasks using Agile Project Management techniques. • Create and maintain a roadmap, aligning tasks with organizational strategy, vision, and business outcomes for the CMM effort. • Facilitate project meetings, document meeting summaries, capture key decisions, and track action items. • Develop and maintain an Integrated Master Schedule (IMS) to include milestones, dependencies, and other details. • Provide biweekly Agile reports such as sprint reports, backlog reporting, sprint burndown charts, sprint retrospective reports, risk registry, etc. • Develop and update the Risks, Assumptions, Issues and Dependencies (RAID) log, and other project management related artifacts. • Perform risk identification and mitigation. • Identify and communicate technical, operational, and business risks; develop mitigation strategies. • Provide transparent reporting, with regular updates to stakeholders on progress, risks, financials, and outcomes. • Provide the COR and government leads with analysis and research, of alternatives, and solution and design options related to the Case Management Modernization effort upon request.

• Seize your opportunity to make a personal impact supporting the Case Management Modernization (CMM) Program • Be part of the CMM Data Modernization and Governance team responsible for delivering an integrated data governance, engineering, data platform, reporting, analytics, and Artificial Intelligence (AI)/Machine Learning (ML) capabilities • Develop, implement, and operationalize a comprehensive data governance framework to accelerate CMM modernization efforts • Ensure alignment with Judiciary policies, practices, and the current Data Governance Roadmap • Drive organizational adoption through robust change management strategies • Guide the design of data standards, oversee daily governance operations, and foster a data-driven culture across the AO and local courts • Develop and maintain an enterprise-level data governance framework tailored to CMM program needs • Create comprehensive data standards, SOPs, and policies covering data quality, metadata, privacy, sharing, classification, and retention • Define and document data classification, access control, privacy, usage, and sharing policies • Use an industry-standard maturity model to assess governance progress, establish milestones, and report status to stakeholders • Implement and operationalize data governance platforms and automated data quality validation and monitoring capabilities • Translate approved data governance strategies into executable operational practices for AO staff and systems • Establish clear responsibilities for data owners, stewards, custodians, and users for monitoring, reporting, and ensuring compliance • Execute change management strategies to ensure sustainable adoption of data governance practices • Develop training modules and stakeholder communication strategies promoting a data-driven culture.

Data Scientist Location New York City Employment Type Full time Location Type Hybrid Department Customer Success Compensation $160K – $190K; Offers Equity Base salary is just one part of the total compensation package and is determined by factors such as leveling, market location, skills, experience, and other considerations. Imagine having the power to stress-test an entire power grid against a hurricane or thunderstorm before the clouds even gather. That is the reality we are creating at Neara. We use advanced machine learning to create engineering-grade, physics enabled digital twins of electricity grids across four continents, this helps asset owners understand their biggest challenges and bring the most viable solutions to life across millions of kilometres of infrastructure. By simulating extreme weather and structural stress at a network-wide scale, we empower the world’s largest utilities to pinpoint risks, optimise investments and build a more resilient global energy future. Our team is a collection of brilliant minds who are fanatical about making a tangible difference in the real world, utilising AI and machine learning to accelerate everything from data classification to complex scenario analysis. We have built a special culture where innovation thrives because everyone owns the mission and we need smart, creative people to help us scale this impact to every corner of the globe. Data Scientist As a Data Scientist, you will analyze a rich array of real-world data to inform our digital twin model of the electric grid, including topography, LIDAR, imagery, vegetation, structural loading, and electrical connectivity. Your work will drive product direction with high visibility, highlight grid expansion opportunities, identify aging and risky infrastructure, and help our customers understand where to build and invest. Working alongside ML Engineers and product-facing engineering teams, your ideas will ultimately take shape as new product features that expand what Neara is capable of doing, and as new infrastructure buildouts for the energy grid itself. What You Will Do: - Model accurate digital twin electric networks from imperfect data using AI, deep learning, and classical ML algorithms. - Surface meaningful analytics and metrics such as wildfire risk that help guide customer buildout of electrical infrastructure. - Advise the company on what our data says and use that understanding to inform Neara’s strategy. - Conduct experiments and A/B tests to improve our modeling of the grid. - QA and improve our predictive models; identify data issues such as distribution drift, overfitting, or test set leakage. - Craft scalable data pipelines to work with a variety of data sources, including LiDAR, aerial photography, photogrammetry and GIS. - Mentor others in best practices for model training, data analytics, and building data-driven products. Who You Are: - A data scientist, ML scientist, or similar with 3-6 years of experience at technical, data-driven companies operating in complex environments. Geospatial data or power grid experience are a plus. - You have a strong intuition for data with good communication skills and experience sharing your findings with customers and senior leaders. - Demonstrated experience with AI and Machine Learning and a keen intuition for data modeling. - Experience translating your models into both experiments and deployable software. - Proficiency in data storage and ETL technologies, such as Parquet, Databricks, Snowflake, PostgreSQL, Spark, and DynamoDB. - Comfort working in an AWS cloud environment. - Excellent problem-solving skills as applied to new domains. - Ability to work effectively asynchronously and cross-functionally on novel, cutting-edge problems. - Ability to own problems and proactively approach challenges. - Prior experience in the energy industry is a plus. What We Offer: - Work with a sophisticated, multi-modal data stack, including LiDAR, satellite imagery, and physics-enabled digital twins, to solve high-stakes engineering problems that most data scientists only see in theory. - Your models will directly prevent wildfires and mitigate disaster risks across millions of kilometers of infrastructure, moving beyond "digital metrics" to harden the real-world energy grid. - You won't just build models; you’ll advise the company on data strategy and see your experiments evolve into core product features that dictate how the world’s largest utilities invest. - We offer a highly competitive compensation package with a significant equity component, ensuring you are a true stakeholder in our mission and benefit directly from the company’s rapid global scale.