AI Applied Data Scientist Jobs

View detail

As a Data Science AI Trainer at Mindrift, you will design original computational data science problems that simulate real-world analytical workflows across various industries including telecom, finance, government, e-commerce, and healthcare. You will create problems requiring Python programming to solve using libraries such as pandas, numpy, scipy, sklearn, statsmodels, matplotlib, and seaborn. These problems must be computationally intensive and not solvable manually within reasonable timeframes. You are expected to develop problems requiring non-trivial reasoning in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction. Your work should produce deterministic problems with reproducible answers, avoiding stochastic elements or using fixed random seeds. Problems will be based on real business challenges such as customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency, spanning the complete data science pipeline including data ingestion, cleaning, exploratory data analysis, modeling, validation, and deployment considerations. You will incorporate big data processing scenarios requiring scalable computational approaches, verify solutions using Python with standard data science libraries and statistical methods, and document problem statements clearly with realistic business contexts and provide verified correct answers.

View detail

Design original computational data science problems simulating real-world analytical workflows across industries such as telecom, finance, government, e-commerce, and healthcare. Create problems requiring Python programming to solve using libraries like pandas, numpy, scipy, sklearn, statsmodels, matplotlib, and seaborn. Ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes. 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, avoiding stochastic elements or using fixed random seeds. Base problems on real business challenges including customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency. Design end-to-end problems covering the complete data science pipeline from data ingestion to 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.

View detail

As a Data Science AI Trainer, you will design original computational data science problems that simulate real-world analytical workflows across various industries including telecom, finance, government, e-commerce, and healthcare. You will create Python programming problems using libraries such as pandas, numpy, scipy, sklearn, statsmodels, matplotlib, and seaborn, ensuring these problems are computationally intensive and cannot be solved manually within reasonable timeframes. You will develop problems requiring complex reasoning in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction, while avoiding stochastic elements or ensuring fixed random seeds for reproducibility. The problems will be based on real business challenges such as customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency, covering end-to-end data science pipeline workflows including data ingestion, cleaning, exploratory data analysis, modeling, validation, and deployment considerations. Your tasks will also involve incorporating big data processing scenarios requiring scalable computational approaches, verifying solutions using Python and standard data science libraries, and documenting problem statements clearly with realistic business contexts along with verified correct answers.

View detail

In this role, you will design and build systems for fraud detection and remediation while balancing fraud loss, cost of implementation, and customer experience. You will work closely with finance, security, product, research, and trust & safety operations to holistically combat fraudulent and abusive actors on the system. Additionally, you will stay abreast of the latest techniques and tools to stay several steps ahead of determined and well-resourced adversaries, and utilize GPT-5 and future models to more effectively combat fraud and abuse.

View detail

As a Senior Data Scientist at Norm, you will join the growing data team to build and scale analytics capabilities. You will develop data models and insights that power the AI compliance engine and help measure, understand, and optimize the product. Responsibilities include designing and maintaining multi-dimensional measurement frameworks for evaluating AI performance across the Norm Law workflow, analyzing telemetry and behavioral logs to understand interactions with AI-generated work, designing and running experiments with rigorous statistical design and clear hypotheses, developing frameworks to measure and communicate performance across key dimensions of legal work quality, implementing product analytics to understand how enterprise clients engage with AI-driven compliance and legal workflows, building self-service analytics capabilities for legal engineers and business stakeholders, contributing to ELT processes using dbt, Airflow, and cloud data warehouses, and helping establish data governance practices that meet enterprise security and regulatory compliance requirements.

View detail

As a Senior Data Scientist, you will lead project teams delivering bespoke algorithms and high-stakes AI solutions to clients, conceive core data science approaches and design robust software architectures for new engagements, mentor a small number of data scientists and support their professional growth, partner with commercial teams to build client relationships and shape project scope for technical feasibility, contribute to Faculty’s thought leadership through courses, public speaking, or open-source projects, and ensure best practices are followed throughout project lifecycles to guarantee high-quality, impactful delivery.

View detail

The Data Scientist on the Preparedness team is responsible for evaluating and improving mitigation systems including classifiers and detection pipelines across various domains such as biosecurity, cybersecurity, and emerging risk areas. They diagnose false positives and false negatives through deep error analysis, root cause investigation, and make clear recommendations for mitigation adjustments. They build monitoring and measurement frameworks to track the effectiveness of mitigations over time and across user segments and use cases. This role involves identifying trends in over-blocking versus under-blocking, quantifying customer impact, and proposing prioritized interventions. The Data Scientist develops insights from customer feedback, complaints, and usage patterns to detect shifts in adversarial behavior and system failure modes. They expand risk monitoring into new areas including cybersecurity threats and scenarios involving model loss-of-control or sabotage in partnership with domain experts. Finally, they communicate results to technical and executive stakeholders using concise narratives, decision-ready metrics, and clear tradeoffs.

View detail

As a Senior Data Scientist on the Data Foundation & AI team at Plaid, you will define and operationalize quality measurement across enrichment and AI systems to ensure products deliver reliable, high-impact outcomes for customers. Responsibilities include owning and evolving evaluation frameworks, investigating customer-facing quality issues, building scalable evaluation systems, measuring AI system effectiveness in a principled way, automating workflows using modern AI tools, and influencing roadmap decisions through data. You will partner closely with product and engineering teams, use data-driven insights to inform decision-making, and raise the quality bar across AI-powered experiences.

View detail

As a Senior Data Scientist at Faculty, you will lead the design and delivery of AI-powered digital twins tailored for each unique Frontier platform deployment, integrating computational twins into real-world decision processes. Your role includes leading data science efforts within cross-functional teams consisting of engineers, designers, and commercial leads to achieve successful project outcomes. You will analyze core customer challenges to ensure every technical solution offers substantial real-world value, perform rigorous exploratory data analysis, model building, validation, and performance monitoring. Additionally, you will support strong client relationships by collaborating with the commercial team to shape strategic project directions. You are also expected to mentor and develop other data scientists through task leadership and possibly line management.

View detail

The Support Data Scientist will explore large support and product datasets to uncover trends, volume drivers, and user-experience pain points, distilling findings into clear, actionable narratives. They will build, enhance, and maintain self-serve dashboards and reporting tools for non-technical teams. The role involves establishing a unified metrics taxonomy for service-health and performance, and building automated data-sharing pipelines and scorecards with BPO partners. They will leverage LLMs to build bespoke classifiers to automatically label and segment inbound volumes, partner with Data Engineering to ensure reliable pipelines and data quality, and document sources of truth. The role also includes conducting deep-dive analyses and delivering strategic recommendations to leadership, prototyping rapidly with tools like ChatGPT and Jupyter notebooks, and collaborating with Data Science on predictive models and experimentation to translate results into operational recommendations.