About Us Fueled by a fundamental belief in innovation, Resurgent Capital Services is an industry-leading financial services company in our sector. It all began 25 years ago when a small group of successful entrepreneurs had a vision for a new type of asset receivables company. One with a commitment to superior service and a personal touch with every interaction. We believe that demonstrating integrity in everything we do, maintaining a strong commitment to compliance, and doing things the right way is a sustainable business model. We want you to feel like your work has an impact and makes a difference every day. Join us as we develop strategies for change and transform the trajectory of your career! Notice for California Residents - California Privacy Policy Summary: The role will first focus on the engineering and operationalization of ML and AI models, ensuring they run reliably in production. As the position evolves, the selected candidate will have opportunities to grow into broader areas of Data Science and Artificial Intelligence. This role is ideal for someone with strong technical foundations who enjoys building systems that make models work reliably in production, with a strong desire to rapidly grow their expertise in Data Science and AI. This position reports to the Sr. Director of Data Science. Roles & Responsibilities: - Package and deploy machine learning models as APIs or batch workflows. - Design pipelines for automated data ingestion, feature processing, and model retraining. - Build monitoring and alerting systems for data quality, drift, and prediction accuracy. - Collaborate with stakeholders across Data Science, IT, and Business teams to ensure smooth integration of models. - Contribute to the development of standards for reproducibility, governance, and compliance. - Explore and introduce new tools in the MLOps ecosystem that improve scalability and reliability. Skills & Qualifications: - Master’s degree in a quantitative field such as Computer Science, Applied Mathematics, or Engineering. - 2–4 years of professional experience in data science, data engineering, or machine learning engineering. - Strong programming skills in Python or R; proficiency with SQL is a plus. - Working knowledge of cloud platforms (preferably Microsoft Azure). - Solid understanding of machine learning, algorithmic thinking, and statistics. - Excellent problem-solving skills and a growth mindset. - Strong communication and collaboration skills. - Familiarity with model deployment frameworks, workflow orchestration tools, or monitoring tools is a plus. Educational Requirements: - Master's degree in quantitative science (mathematics, computer science, engineering, etc.) *This is a 100% remote role. Resurgent is an Equal Opportunity employer that is fueled by our diverse and inclusive work environment. Are you excited about this opportunity, but your skills and experience aren't an exact match? We encourage you to apply anyway! You may be just the person we are searching for to fill this or another position. We would love to consider you for the Resurgent team! All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, age, national origin, disability, protected veteran status, gender identity or any other factor protected by applicable federal, state, or local laws.

You are collaborative, proactive, and motivated by solving complex, real world problems with AI. You thrive in fast‑paced, cross‑functional environments, balance innovation with delivery. Building production grade machine learning systems that have measurable clinical and business impact energizes you. - Ability to Deliver Results: Proven success delivering AI solutions that improve operational efficiency, product quality, and user outcomes in regulated, high‑stakes environments. - Comfort with Ambiguity: Strong ability to structure open‑ended problems, define success metrics, and make steady progress amid evolving requirements. - Passion for Innovation: Deep interest in applied AI, computer vision, and emerging ML techniques, with a focus on translating research into reliable products. - Positive Outlook: Consistently finds opportunities within technical, regulatory, and operational constraints. - Strong Communication Skills: Able to clearly explain complex models and results to clinicians, engineers, product leaders, and executives. - Bias for Action: Makes informed decisions quickly, prototypes rapidly, and iterates based on data and feedback. What You Will Do You will create and extend applied AI and ML solutions that enable intelligent, real-time decisions in complex, regulated environments. You will collaborate with engineering, product, and domain experts to deliver reliable, scalable, and compliant ML solutions from concept through production. - Execute end‑to‑end data science, from problem framing and data strategy to model development, deployment, and optimization. - Design, train, and evaluate machine learning and deep learning models for real‑time and near‑real‑time inference. - Apply techniques across computer vision, pattern recognition, predictive modeling, and generative AI to solve domain specific problems where accuracy, latency, and robustness are critical. - Translate ambiguous domain and business requirements into clear methods, success metrics, and deployable systems. - Build scalable data pipelines and feature engineering workflows using Python or statically compiled languages. - Partner with engineering teams to integrate models into production systems with a focus on performance, reliability, and operational constraints. - Implement monitoring, validation, and retraining strategies to manage drift and model performance. - Use statistical methods and experimentation techniques to assess model quality, measure impact, and guide iterative improvements. - Work within governance, privacy, and compliance requirements to ensure models meet organizational and regulatory standards. - Evaluate emerging ML techniques and apply innovative approaches where they meaningfully improve outcomes. - Improve code quality, testing, documentation, and reproducibility across data science and ML workflows. - Communicate technical insights and recommendations effectively to both technical stakeholders and nontechnical decision makers. This Opportunity Could Be for You If - You hold a master’s degree in computer science, electrical or biomedical engineering, statistics, or a related field with a focus on machine learning or computer vision. - Possess a fundamental understanding of linear algebra, quaternions, and 3D geometry. - Possess a fundamental understanding of the operation and nuances of image sensors, lens optics, and the camera calibration process. - Experience generating intrinsic and extrinsic camera matrices and estimating camera position and pose. - Experience optimizing algorithms for embedded systems and servers. - You have at least three years of experience as a data scientist, machine learning engineer, AI research engineer, or in an equivalent role. - You have delivered data science projects from problem definition through production deployment. - You are skilled at building ML models and working with large, complex datasets. - You collaborate effectively with cross‑functional teams and translate requirements into solutions. - You apply advanced statistical techniques to uncover patterns, trends, and strategic insights. - You proactively check model performance and pursue continuous improvement. - You stay current on developments in AI, machine learning, and data engineering and look for opportunities to introduce novel approaches. Preferred Technical Qualifications Candidates should show strength in the following areas: - Experience designing, training, validating, and deploying deep learning models in production environments. - Practical experience in the use of OpenCV, and PyTorch or TensorFlow. - Strong background in computer vision, video analytics, or medical image analysis. - Ability to build scalable data pipelines and labeling workflows using modern data engineering frameworks. - Experience deploying models using cloud and edge infrastructure, with an understanding of latency and resource constraints. - Solid grounding in statistical methods, experimentation design, time series analysis, and model evaluation. - Familiarity with MLOps and DevOps practices, including CI/CD for ML, containerization, monitoring, and model lifecycle management. - Production development in Python or statically compiled languages, and SQL for large scale analysis and model development. - Experience working with regulated data and knowledge of healthcare privacy and medical device software standards is a plus. - Effective communication skills and the ability to collaborate effectively with clinicians, engineers, product leaders, and regulatory partners. Fortive Corporation Overview Fortive’s essential technology makes the world stronger, safer, and smarter. We accelerate transformation across a broad range of applications including environmental, health and safety compliance, industrial condition monitoring, next-generation product design, and healthcare safety solutions. We are a global industrial technology innovator with a startup spirit. Our forward-looking companies lead the way in software-powered workflow solutions, data-driven intelligence, AI-powered automation, and other disruptive technologies. We’re a force for progress, working alongside our customers and partners to solve challenges on a global scale, from workplace safety in the most demanding conditions to groundbreaking sustainability solutions. We are a diverse team 18,000 strong, united by a dynamic, inclusive culture and energized by limitless learning and growth. We use the proven Fortive Business System (FBS) to accelerate our positive impact. At Fortive, we believe in you. We believe in your potential—your ability to learn, grow, and make a difference. At Fortive, we believe in us. We believe in the power of people working together to solve problems no one could solve alone. At Fortive, we believe in growth. We’re honest about what’s working and what isn’t, and we never stop improving and innovating. Fortive: For you, for us, for growth. About Provation Provation is a leading provider of healthcare software and SaaS solutions for clinical productivity, care coordination, quality reporting, and billing. Our purpose is to empower providers to deliver quality healthcare for all. Provation’s comprehensive portfolio spans the entire patient encounter, from pre-procedure through post-procedure, with solutions for physician and nursing documentation (Provation® MD, Provation® Apex, MD-Reports, Provation® endoPRO®, and Provation® MultiCaregiver), anesthesia documentation (#1 Best in KLAS Provation® iPro), patient engagement, surgical care coordination, quality reporting, and billing capture (Provation® SurgicalValet™), order set and care plan management (Provation® Order Set Advisor™ and Provation® Care Plans), and EHR embedded clinical documentation (Provation® Clinic Note). Provation has a loyal customer base, serving more than 5,000 hospitals, surgery centers, and medical offices, and 700 physician groups globally, including 19 of the top 20 U.S. hospitals. In 2021, Provation was acquired by Fortive Corporation, a Fortune 1000 company that builds essential technology and accelerates transformation in high-impact fields like workplace safety, engineering, and healthcare. For more information about our solutions, visit provationmedical.com and follow us on Twitter, Facebook, and LinkedIn. Our purpose at Provation is to empower providers to deliver quality healthcare for all. To deliver on this commitment, we’re guided by our core values – Provation has a culture of CARES: Community - We have a shared sense of improving healthcare, enriching the broader world we live in and serve. Accountability - We own it and get it done with integrity. Respect - We build diverse teams that collaborate and communicate with positive intent and trust. Excellence - We welcome new ideas as we innovate quality solutions. Service - We are passionate about putting customers first. We Are an Equal Opportunity Employer Fortive Corporation and all Fortive Companies are proud to be equal opportunity employers. We value and encourage diversity and solicit applications from all qualified applicants without regard to race, color, national origin, religion, sex, age, marital status, disability, veteran status, sexual orientation, gender identity or expression, or other characteristics protected by law. Fortive and all Fortive Companies are also committed to providing reasonable accommodations for applicants with disabilities. Individuals who need a reasonable accommodation because of a disability for any part of the employment application process, please contact us at applyassistance@fortive.com.

• Serás referente técnico dentro del equipo, proponiendo mejoras en la estrategia de medición y en los procesos analíticos, tanto a nivel interno como ante el cliente. • Diseñarás, configurarás y auditarás implementaciones de analítica digital complejas, garantizando la calidad y consistencia del dato desde la capa técnica hasta su explotación en dashboards o modelos de negocio. • Acompañarás y asesorarás a los clientes en la definición de sus ecosistemas de medición, aportando una visión estratégica sobre cómo los datos pueden mejorar su rendimiento digital. • Colaborarás con equipos multidisciplinares (desarrollo, marketing, negocio) para alinear la medición con los objetivos estratégicos y facilitar la toma de decisiones basadas en datos. • Identificarás oportunidades de optimización, automatización y mejora continua, liderando iniciativas técnicas que impulsen la innovación y eficiencia de los proyectos. • Diseñarás, configurarás y auditarás implementaciones de medición avanzada en entornos web y app (Android/iOS), utilizando Google Analytics 4, Firebase y herramientas de etiquetado como Google Tag Manager y Google Tag Manager Server-Side. • Diseñarás arquitecturas de tagging personalizadas y modelos de medición basados en KPIs y funnels de negocio. • Integrarás datos de múltiples fuentes, incluyendo el consumo de APIs externas para automatizar reporting o enriquecer datasets analíticos. • Utilizarás diferentes plataformas para combinar la analítica cuantitativa con insights cualitativos sobre la experiencia digital. • Diseñarás dashboards interactivos en Looker Studio o Power BI, conectando datos desde GA4, BigQuery u otras fuentes vía API.

• Ideate, develop and improve machine learning and statistical models that drive Swish’s core algorithms for producing state-of-the-art sports betting products. • Develop contextualized feature sets using sports specific domain knowledge. • Contribute to all stages of model development, from creating proof-of-concepts and beta testing, to partnering with data engineering and product teams to deploy new models. • Strive to constantly improve model performance using insights from rigorous offline and online experimentation. • Analyze results and outputs to assess model performance and identify model weaknesses for directing development efforts. • Adhere to software engineering best practices and contribute to shared code repositories. • Document modeling work and present to stakeholders and other technical and non-technical partners.