Lead the development and execution of AI and machine learning strategy for biometric face verification and security.
Compensation
Comp: Negotiable (Base) + Company Performance Bonus (20%) + EMI Share Options + UK iProov Benefits
Responsibilities
Define and steward the AI strategy, roadmap, and vision across core AI research, product, and production domains
Lead teams across research, production/engineering, and MLOps to deliver robust, secure, explainable models for liveness assurance and fraud detection
Oversee model development and deployment pipelines: data ingestion, training, optimization, evaluation, monitoring, and continuous improvement in adversarial biometric threat landscapes
Ensure AI is aligned with product and commercial goals, measuring ROI and outcomes of AI deployments
Drive partnerships and external collaboration with academic, industry, and regulatory stakeholders
Requirements
Master’s or PhD in computer science, machine learning, data science, or a AI related field
Define and maintain the enterprise AI architecture blueprint, standards, and technical roadmap.
Design and oversee development of AI models, pipelines, and deployment environments.
Architect AI and machine learning solutions for CPG use cases such as demand forecasting, trade promotion optimization, and consumer and shopper insights across HeartlandFPG brands.
Lead technical evaluation and selection of AI platforms, tools, and frameworks, including ongoing stewardship of the Abacus.AI platform.
Ensure AI architecture aligns with enterprise data platform design managed through Microsoft Fabric.
Requirements
Bachelor's Degree in Computer Science, Data Science, Mathematics, Engineering, or related field
Lead the Data Science team, driving high-impact projects, mentoring staff, and shaping the technical direction of data science and machine learning solutions.
Locations (2)
São Paulo, Brazil
Brazil
Compensation
Competitive salary
Profit sharing
Responsibilities
Manage a team of Data Scientists and Machine Learning Engineers
Act as a technical reference, architecting, developing, and deploying machine learning systems
Discuss business requirements with Product Managers and senior leaders
Guide the team in designing and executing experiments
Recruit and develop talent
Requirements
MSc. or PhD in Computer Science, Statistics, or a relevant technical field
Nice to have: Large Language Models (RAG, fine-tuning)
Nice to have: AI agents or agentic frameworks (LangGraph, LangChain)
Nice to have: experience with fast-growing startups or high-tech companies
Help shape the future of manufacturing by transforming complex engineering and production challenges into data-driven solutions that create measurable business value.
Responsibilities
Analyse manufacturing data to uncover patterns, identify root causes and drive process improvements
Develop and validate AI/ML models and advanced analytics solutions for quality improvement, anomaly detection, predictive maintenance, productivity and yield improvement, process stability monitoring, and decision support
Collaborate closely with Process Experts, Technology Development Engineers, Data Analysts, MES/PLC specialists, Data Engineers, MLOps teams, IT professionals and business stakeholders
Contribute throughout the entire solution lifecycle from problem definition and data exploration to model development, validation, deployment support and continuous improvement
Help standardize and reuse analytical methods, models and best practices across projects and locations
Requirements
MSc or PhD in Data Science, Artificial Intelligence, Machine Learning, Computer Science, Mathematics, Statistics, Engineering, Physics or a related field
Requirements
Proficiency in Python and PySpark for data analysis, machine learning, and demand forecasting (regression + time series models)
Experience with forecasting models such as LightGBM/ XGBoost and classical time series methods (e.g., ETS/ARIMA)
Strong understanding of the machine learning workflow (data cleansing, feature engineering, model evaluation, model explainability)
Familiarity with forecasting evaluation metrics (e.g. MAPE, MAE) and ability to validate model performance
Strong SQL skills for managing and querying large datasets
Knowledge of data visualization tools (e.g., Power BI)
Experience with cloud-based technologies such as Databricks, Azure, or AWS
Strong communication skills — able to explain insights and models to both business and technical teams
Ownership and accountability — able to deliver end-to-end solutions, not just analysis
Collaboration — able to work effectively with Data Engineering, Tech, Product, and Supply Chain teams
Preferred (Optional) Qualifications:
Exposure to MLOps tools (e.g., MLflow, job scheduling)
Experience building production pipelines for forecast outputs (daily/weekly runs) and supporting downstream systems
Experience in real-time analytics or scheduled processing systems
Optimization mindset — able to balance accuracy, business impact, and time constraints
Clear focus.
Diverse Workplace (Our members are from around the world!)
Non-hierarchical and agile environment
Growth opportunity and career path
Responsible for conducting quantitative analytics and modeling projects for specific business units or risk types within Global Risk Analytics, developing models, processes, and documentation to support risk and capital management.
Locations (3)
Charlotte, United States
Chicago, Illinois, United States
Jersey City, New Jersey, United States
Compensation
$89,800.00 - $151,200.00 annualized salary, offers to be determined based on experience, education and skill set. Discretionary incentive eligible. This role is eligible to participate in the annual discretionary plan.
Responsibilities
Performs end-to-end market risk stress testing including scenario design, implementation, results consolidation, internal and external reporting, and analyzes stress scenario results
Supports planning related to setting quantitative work priorities in line with the bank’s overall strategy and prioritization
Identifies continuous improvements through reviews of approval decisions on relevant model development or model validation tasks
Supports model development and model risk management in respective focus areas to support business requirements and the enterprise's risk appetite
Supports methodological, analytical, and technical guidance to influence strategic direction and tactical approaches of development/validation projects
Requirements
Master’s degree in Math, Economics, Statistics, or similar discipline
PhD in Math, Economics, Statistics, or similar discipline
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