• Develop and implement machine learning models (classification, regression and segmentation) using a variety of Python libraries; • Validate machine learning models using diverse metrics and concepts such as confusion matrix, backtesting, out-of-time testing, train/test splits, cohorts, ROC-AUC and KS; • Handle data from different sources, consolidating it into usable structures for analysis. Data may not always be in the same environment or in the ideal format for model application; • Produce activity status updates as well as clear data process documentation to support solutions at the strategic level; • Create storytelling and prepare presentations to communicate modeling and analysis results; • Contribute to process changes by improving them through data to deliver measurable financial impact to the company; • Collaborate with Data Infrastructure, Data Engineering and Systems, and Process Engineering teams to integrate analytical solutions into existing internal tools and processes as effectively as possible; • Monitor and propose improvements in the area, targeting process enhancements related to activities, projects and initiatives to incorporate industry best practices.

• Take scoped engagements through staffing, production, QC, and delivery. Own timeline, throughput, and the delivered package across several concurrent projects at once. • Staff each project from the expert pool, brief and calibrate contributors, manage their output and utilization, and keep production moving without quality drift. • Run the QC structure on every deliverable — calibration layers, severity-weighted rubrics, deterministic verifiers, evidence maps — so each package survives buyer-side scrutiny before it ships. • Track output-per-contributor-week, flag bottlenecks early, and protect margin by managing expert hours against scope. • Keep the Strategic Project Lead and the Human Data Evals Lead informed on status, risk, and what shipped; surface anything that needs escalation before it becomes a delivery miss.

Role Description Data scientists at Thoughtworks are strategic leaders who spearhead data-driven initiatives, tackle complex business challenges and uncover transformative insights. They possess a deep understanding of a client's business ecosystem and partner with executives to align technology strategies with business objectives. By contextualizing emerging trends and Thoughtworks' exploration, they expand the impact of data science within the client organization. As a data scientist at Thoughtworks, you'll leverage your deep technical knowledge to solve complex business problems, making a significant impact on client success. Job Responsibilities - Establish a clear vision and direction for the AI paradigm/practices, aligning it with the overall business strategy. - Collaborate with executive level stakeholders to understand their strategic objectives and identify opportunities to leverage data and data quality for enabling AI and machine learning. - Be responsible for the overall AI system design, problem framing and governance, identifying and managing risks and issues. - Identify and prioritize data science projects that align with business objectives and ensure that projects are completed on time and within budget. - Design experiments for the team to rapidly test ideas and assumptions, and identify future courses of action. - Get involved with data science delivery whenever needed. - Communicate technical findings and insights to stakeholders in a clear, easy and concise way, keeping the stakeholders in mind. - Build and drive the growth of the data science community along with promoting a culture of 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 the Retail and Customer Analytics industry. - Experience with defining strategy/vision for the data science function, aligning it with the overall business strategy. - Experience in leading the analysis and modeling of different datasets and overseeing an inception to deploying and productionizing the models. - Experience with statistical modeling, machine learning, deep learning, optimization and other data science techniques to implement end-to-end data science projects. - Understanding of the specialized areas of AI and possess a specialty with at least one of them (i.e.: NLP/computer vision/Generative AI, etc). - Experience with R, Python or equivalent statistical/data analysis tools and can write production-ready code that is easy to evolve and test. - Gather and preprocess large volumes of structured and unstructured data from various sources, ensuring data quality and integrity. - Develop and implement machine learning models, algorithms and predictive analytics to solve specific business problems or improve existing processes. - Evaluate model performance using appropriate metrics and validate models to ensure robustness and reliability. - Interpret and effectively communicate findings and insights to non-technical stakeholders through reports, presentations and visualizations. Requirements - Understand the importance of stakeholder management and can easily liaise between clients and other key stakeholders throughout projects, ensuring buy-in and gaining trust along the way. - Resilient in ambiguous situations and can adapt your role to approach challenges from multiple perspectives. - Don’t shy away from risks or conflicts, instead take them on and skillfully manage them. - Eager to coach, mentor and motivate others and aspire to influence teammates to take positive action and accountability for their work. - Enjoy influencing others and always advocate for technical excellence while being open to change when needed. - Proven leader with a track record of encouraging teammates in their professional development and relationships. - Cultivating strong partnerships comes naturally; understand the importance of relationship building and how it can bring new opportunities to the business. Benefits - Learning & Development: There is no one-size-fits-all career path at Thoughtworks; your career development is supported by interactive tools, numerous development programs, and teammates who want to help you grow. - Responsible Use of AI in Recruitment: AI tools are used to support recruitment tasks, but do not screen, assess, rank or make hiring decisions. - Commitment to providing reasonable accommodations to qualified applicants with disabilities or sincerely held religious beliefs, practices, or observances. 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. For 30+ years, we’ve delivered extraordinary impact together with our clients by helping them solve complex business problems with technology as the differentiator. Bring your brilliant expertise and commitment for continuous learning to Thoughtworks. Together, let’s be extraordinary. Salary $159,000 — $309,000 CAD

Role Description Onpoint Healthcare Partners builds Iris, a Medical Agent AI platform that takes administrative work off the plates of providers and care teams. Our agents handle charting, coding, care gap closure, and care coordination across the full patient journey, and customers trust them because we hold the platform to clinical accuracy standards and back them up with evidence. That trust is earned with data. We are seeking a Staff Data Scientist to be the person who proves our AI is right. You will drive data science, analytics, and AI evaluation across our clinical and operational platforms: - Measuring agent quality - Building the evaluation frameworks behind our accuracy standards - Analyzing claims, clinical, and EHR data to find opportunities to improve outcomes, quality, and operational efficiency The ideal candidate understands healthcare data at scale and partners closely with Product, Engineering, Clinical Operations, and AI teams. Qualifications - 7+ years of experience in Data Science, Analytics, Machine Learning, or a related field - Strong experience working with healthcare data including claims, clinical, EHR, or population health datasets - Familiarity with standards such as HL7, FHIR, and ICD-10 - Strong proficiency in SQL and Python - Experience evaluating AI and ML systems and defining quality metrics - Strong foundation in statistics, experimentation, and predictive modeling - Ability to communicate insights clearly to business and technical stakeholders - Working knowledge of HIPAA and PHI handling requirements Requirements - Analyze claims, clinical, EHR, and operational datasets to identify trends, risks, and opportunities - Apply healthcare data standards and vocabularies such as HL7, FHIR, ICD-10, CPT, SNOMED, and LOINC when normalizing and linking data - Develop statistical models and analytical frameworks to support product and business decisions - Design and maintain AI evaluation frameworks including rubrics, golden datasets, benchmark suites, and regression testing - Measure AI agent quality, accuracy, precision, recall, and business impact - Conduct exploratory data analysis and communicate findings to technical and business stakeholders - Partner with clinical and operational teams to define meaningful success metrics - Support data quality initiatives and ensure reliability of data used for analytics and AI systems - Apply advanced SQL, Python, statistics, and machine learning techniques to solve business problems - Protect PHI in all data handling and ensure compliance with regulatory requirements Benefits - Improved AI quality, accuracy, and clinical relevance - Actionable insights that drive business and operational outcomes - Trusted data assets and evaluation frameworks used across the organization - Strong partnerships across Product, Clinical, Data, and Engineering teams Physical Demands The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. While performing the duties of this job, the employee is regularly required to speak, hear, read, and type. This is largely a sedentary role. This position requires the ability to occasionally lift office products and supplies up to 40 pounds. Work Environment To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed above are representative of the knowledge, skill and/or ability required. Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties, or responsibilities that are required of the employee for this job. Duties, responsibilities, and activities may change at any time, with or without notice.