• Own the end-to-end oversight of the client’s monthly and/or quarterly data submission cycle, ensuring all third-party data providers are progressing accurately and on time. • Lead and direct a team responsible for day-to-day submission tracking, provider outreach, and workflow management. • Establish and oversee a structured internal review process for data exceptions, ensuring issues are thoroughly assessed and remediated before reaching the client. • Drive resolution of data quality issues across real estate financial data types including Trial Balances, General Ledgers, loan data, aging schedules, and property-level reporting. • Serve as the final internal decision-maker on escalations, ensuring the client receives only well-documented, fully vetted issues. • Oversee source-specific data mapping for providers submitting raw financial data, Oversee raw data intake and mapping to client systems, ensuring field-level accuracy and continuity as source systems evolve. • Partner with internal technical teams to maintain data integrity across all provider submissions on the Cherre platform. • Manage the assignment, configuration, and ongoing governance of standardized reporting templates across a diverse provider base and asset class portfolio. • Maintain and evolve business rule validation logic, proactively identifying gaps and driving improvements in data quality standards. • Serve as the senior escalation point for third-party data providers, including property managers, fund administrators, and asset managers. • Build and maintain strong provider relationships, ensuring a high-quality, responsive support experience through a well-managed team. • Champion a tech-forward approach to service delivery, leveraging the Cherre platform and agentic AI tools to continuously improve efficiency and provider experience. • Own the enterprise account Information Provider Standard Operating Procedures, ensuring documentation is current, comprehensive, and scalable. • Own enterprise account performance reporting, delivering actionable insights on submission health, data quality trends, and operational performance to client stakeholders. • Maintain governance standards across user access, escalation tracking, and data quality logging in alignment with enterprise account requirements.

Role Description Data scientists at Thoughtworks are responsible for leading and executing data science projects. They employ their profound expertise in statistics, modeling, machine learning algorithms, and data mining methodologies to render business value and illuminate data-driven insights. - Lead and manage data science projects from inception to completion, including goal-setting and scope definition. - Collaborate with stakeholders to understand strategic objectives and identify opportunities to leverage data for AI and machine learning. - Responsible for overall AI system design, problem framing, governance, and risk management. - Design, develop, and implement data science strategies that drive measurable business results. - Design experiments for rapid testing of ideas and assumptions. - Select machine learning models for production deployment using quality metrics. - Communicate technical findings and insights to stakeholders clearly and concisely. - Contribute to the growth of the data science community and promote data literacy within the organization. - Address ethical concerns and adhere to ethical guidelines using FATTER AI (fairness, accountability, transparency, trustworthiness, explainability, responsibility). - Work with CD4ML practices and data versioning tools. Qualifications - Experience in leading the analysis and modeling of different datasets and overseeing model deployment. - Experience with statistical modeling, hypothesis testing, machine learning, deep learning, and optimization. - Specialty in at least one area of AI (e.g., NLP, computer vision, Generative AI). - Experience with R, Python, or equivalent statistical/data analysis tools. - Ability to gather and preprocess large volumes of structured and unstructured data. - Analyze datasets using statistical and visualization techniques. - Develop and implement machine learning models and predictive analytics. - Evaluate model performance and validate models for robustness. - Interpret and communicate findings to non-technical stakeholders. - Ensure compliance with data privacy regulations and ethical standards. Requirements - Understand the importance of stakeholder management and liaise effectively between clients and stakeholders. - Resilient in ambiguous situations and adaptable to challenges. - Proactively manage risks and conflicts. - Eager to coach, mentor, and motivate others. - Advocate for technical excellence while being open to change. - Proven leader with a track record of encouraging professional development. - Strong relationship-building skills to create new opportunities. Benefits - Autonomy in career development supported by interactive tools and development programs. - Supportive teammates who want to help you grow. Company Description Thoughtworks is a dynamic and inclusive community of bright and supportive colleagues who are revolutionizing tech. As a leading technology consultancy, we’re pushing boundaries through our purposeful and impactful work. - Over 30 years of delivering extraordinary impact with clients. - Helping clients solve complex business problems with technology as the differentiator. - Commitment to continuous learning and professional growth.

• Define and execute the analytical framework for evaluating agent and channel performance across a multichannel distribution model • Design and implement predictive models supporting growth opportunity identification, customer experience, retention risk, and channel optimization • Conduct program evaluation using experimental and quasi-experimental methods to establish causal impact of business initiatives • Translate analytical findings into clear, actionable recommendations for senior leadership • Set and prioritize the analytical agenda independently, focusing on the highest-value questions • Operate effectively in a data environment that is not fully controlled or curated, maintaining rigor despite imperfect inputs

• Directly manage a team of highly skilled data scientists. Start from 2-3 and grow to 5-6. • Act as a technical mentor that can dive deep and provide detailed direction. • Lead planning, resourcing and communications with senior leadership, product and engineering teams. • Develop strong business intuition and guide your team to deliver performant DS solutions on aggressive timelines. • Develop and maintain SentiLink’s fraud detection models through the full model development lifespan: from data acquisition decisions through featurization, focusing labeling resources, model training, experimentation, productionalization, and monitoring. • Research new types of fraud and develop new SentiLink products around identity verification. • Achieve success by researching / developing through iteration, integration of new data sources and inventive feature engineering. • Write production-ready code that can be relied on for real-time decision making by our partners. • Design, perform, and present analyses that will inform data acquisition, product development, risk operations priorities, marketing, and sales efforts.