• Lead the design, development, and deployment of ML and graph-based algorithms for international entity resolution, identity trust scoring, and anomaly detection across heterogeneous, country‑specific datasets. • Architect reusable matching and linking frameworks that work across multiple ID schemes (e.g., national ID numbers, passports, voter IDs, mobile accounts, bank accounts) and local name/address conventions. • Develop probabilistic and rule‑augmented models that handle noisy, sparse, or partially labeled international data while maintaining explainability and regulatory defensibility. • Define and evolve the international extension of Socure’s identity graph: schema design, linkage strategies, quality tiers, and confidence scoring that can be leveraged by multiple products (Verify, KYC, watchlists, fraud). • Design and implement robust data quality and monitoring frameworks for international identity data (coverage, stability, drift, regional bias, label quality) and integrate them into modeling and production monitoring workflows. • Own experimentation strategy for major international eKYC initiatives: Design offline evaluations and online A/B tests that reflect local ground truth constraints and data sparsity. • Define success metrics that balance approval rates, fraud capture, and regulatory/operational constraints per market. • Analyze lift, stability, and fairness trade‑offs and drive go/no‑go decisions with Product and Engineering. • Contribute to model governance documentation and support responses to regulators and large enterprise customers regarding model logic, data provenance, fairness, and monitoring for international markets.

• Own the design, development, and evaluation of machine learning, statistical, and graph-based algorithms for entity-resolution, identity trust scoring, and anomaly detection on massive datasets. • Architect and optimize graph-based identity representations (identity graph structure, linkage rules, clustering) to improve match rates, reduce false positives/negatives, and support downstream fraud and KYC models. • Build and maintain scalable data pipelines and feature stores in Spark/PySpark (or Scala), including data normalization, deduplication, and feature computation across large PII datasets in AWS/Databricks environments. • Lead A/B tests and offline/online experimentation for new models, features, and data sources; define success metrics, design experiments, and ensure rigorous validation before rollout. • Evaluate new internal and external data sources: explore signal quality, design backtests, quantify incremental value, and provide clear recommendations on vendor selection and integration. • Partner closely with product managers and engineers to translate ambiguous business and regulatory requirements (e.g., KYC coverage, watchlist matching) into concrete modeling and data roadmaps. • Provide deep analytical support to Socure’s compliance and regulatory product suite, including investigative analyses, root‑cause analysis for anomalies, and clear narratives for internal and external stakeholders. • Contribute to model governance and documentation: clearly explain model logic, data dependencies, limitations, and monitoring plans to internal risk/compliance stakeholders. • Mentor junior data scientists and engineers on best practices in data exploration, feature engineering, experimentation, and code quality. • Communicate complex technical concepts and trade‑offs in a concise, structured way to both technical and non‑technical audiences (e.g., product reviews, customer meetings, internal briefings).

• Lead cross-functional initiatives to define, implement, and iterate on measurement and analysis of our Virtual Power Plant (VPP). • Proactively analyze and interpret complex data to uncover critical business, product, and user insights. Propose strategies for improving both the systems powering our VPP and the tools used by our energy industry partners. • Synthesize large-scale, disparate datasets — including smart meter data, device telemetry, settlement data, and logs — to aid in increasing the impact of our VPP products and services. • Build source of truth data models in DBT that serve as the foundation for reporting and decision-making. • Define and monitor KPIs to evaluate the success of new product features and business initiatives. • Influence product and business strategy by effectively communicating analytical results, recommendations, and tradeoffs to stakeholders across all levels of the organization. • Partner with engineers to define appropriate data instrumentation for new product features. • Perform statistical analysis and build predictive models on user interactions and smart device data to drive business recommendations and product strategy. • Contribute to the culture and workflows of the Product Analytics team – advocate for analytic best practices, introduce new tools and processes, perform code reviews, support your peers, and coach more junior members.

• Coordinate conversion activities, translate and document conversion requirements and detailed plans. • Drive execution tasks with various team members and stakeholders, track progress, and escalate issues. • Follow project management processes and best practices to ensure all tasks, activities, and resources are aligned to meet the overall program and organizational goals. • Work cross functional to complete Build Phase, support system configuration design, testing, migration and hyper-care activities. • Completion of conversion functional specification documents, data mapping, cross reference documents and data model documents across multiple deployments. • Drive conversion progress, including task completion, milestone achievement to adhere to overall timeline. • Lead meetings with cross functional thread leads. • Understanding team member workload, potential resource constraints, and ensure optimal resource utilization throughout the conversion process. • Proactive risk management: identify, assess, mitigate, and monitor potential risks with real-time alerts for critical issues. • Monitor and track conversion and deviation issue resolution. • Communicate expectations and instill accountability in team members. • Resolve conflicts, promote work sharing, and motivate teams toward common goals. • Manage multiple conversion activities, tasks and resources through effective organization prioritization, and time management practices to meet program objectives. • Additional responsibilities include documentation, profiling analytics, reporting, internal stakeholder communication, and identifying areas for improvement to enhance quality and efficiency of cutover activities.