Role DescriptionHow many times do you get the opportunity to be on the ground floor of a big and important mission? What if you could be one of the top contributors defining the mission, guiding our teams, and influencing the direction of Dropbox’s AI-first journey? As the Sr Data Science Manager for Dash Experience, you will get to do exactly that. You will join a team of top-tier data scientists and become an integral part of the product organization, helping to scale this new business. Joining on the ground floor of this startup team, you’ll partner directly with the Head of Data Science and Product, Engineering and Design leadership to shape the product roadmap, foster a top-tier, data-informed culture, and drive real AI/ML impact and execution along the way! Responsibilities - Manage and scale a high performing team of data scientists - Coach and mentor data scientists of varying experiences to ensure their continued growth - Plan, execute, and deliver a profolio of mission-critical data science projects for Dash Experience, and provide technical leadership in a fast-paced environment - Leverage data-driven insights to proactively identify most impactful opportunities, and directly influence product roadmaps and strategies - Translate complex data insights into implications and recommendations for the business via excellent communication skills, both verbal and written - Work with cross-functional teams (including Product, Engineering, Design, User Research, and senior executives) to rapidly execute and iterate - Partner with Product Engineers and Data Engineers to build the reliable, efficient, and scalable data foundations, tools, and processes to drive our AI/ML capabilities’ long-term growth - Identify what matters most and prioritize ruthlessly Requirements - Bachelors’ or above in quantitative discipline: Statistics, Applied Mathematics, Economics, Computer Science, Engineering, or related field - 10+ years experience of leveraging data-driven analysis to influence product roadmap and business decision, preferably in a tech company - 3+ years of experience directly managing data scientists or product analysts - Proven track record of managing a portfolio of Data Science projects that improve user experience and drive measurable business impact - Deep understanding of statistical analysis, experimentation design, and common analytical techniques - Experience using data to inform product and business decisions that led to measurable outcomes - Exceptional verbal and written communication skills Preferred Qualifications - Experience in startups or building 0→1 products - Expertise in using data to inform AI/ML product development - Background in SaaS product CompensationUS Zone 1 $233,800—$316,300 USD US Zone 2 $210,400—$284,600 USD US Zone 3 $187,000—$253,000 USD

Company Description Nexthink is the leader in digital employee experience management software. The company provides IT leaders with unprecedented insight allowing them to see, diagnose and fix issues at scale impacting employees anywhere, with any application or network, before employees notice the issue. As the first solution to allow IT to progress from reactive problem solving to proactive optimization, Nexthink enables its more than 1,300 customersto provide better digital experiences to more than 18 million employees. Dual headquartered in Lausanne, Switzerland and Boston, Massachusetts, Nexthink has 9 offices worldwide. Job Description We are seeking a skilled and intellectually curious Data Analyst / Engineer to join our team. In this role, you will design and maintain robust data pipelines, transform complex datasets into actionable insights, and build the analytical infrastructure that drives strategic decision-making. You are autonomous, analytically minded, and thrive in environments where the problem is not always well-defined. Key Responsibilities Data Engineering & ETL - Design, develop, and maintain end-to-end ETL/ELT pipelines to integrate data from multiple sources. - Ensure data quality, consistency, and reliability through validation, cleansing, and monitoring processes. - Build and optimize data models to support reporting and analytics at scale. Data Analysis & Visualization - Conduct in-depth analysis to uncover trends, patterns, and business opportunities. - Develop interactive Power BI dashboards and reports that communicate insights clearly to technical and non-technical stakeholders. - Apply DAX calculations and Power Query transformations to deliver performant, maintainable reports. Performance Monitoring & Reporting - Define, track, and report on key performance indicators (KPIs) across business units. - Proactively identify opportunities to improve data processes and reporting efficiency. - Optimize reports and data models for scalability across large datasets. Cross-Functional Collaboration - Partner with teams across the organization to translate business needs into data solutions. - Act as a subject matter expert in data engineering and analytics, supporting colleagues with guidance and best practices. - Participate in interdisciplinary projects that require both technical depth and business acumen. Qualifications Required - 2+ years of experience in a data analyst, data engineer, or similar role. - Proven experience with ETL/ELT pipeline development. - Advanced proficiency in DAX and Power Query for data modeling and report development. - Strong SQL skills for querying, transforming, and managing relational data. - Strong Python skills, including data manipulation, automation, and pipeline scripting; experience with testing and code quality practices is a plus. - Experience building interactive Power BI reports from multiple data sources. - Analytical, curious, and autonomous - comfortable navigating ambiguous problems independently. - Strong communication skills, with the ability to present findings to both technical and business audiences. - Fluency in English (additional languages are a plus). Preferred - Experience with Microsoft Fabric. - Familiarity with Azure Data Factory for orchestrating data workflows. - Exposure to Azure Synapse Analytics for large-scale data processing. Additional Information We are the pioneers and trailblazers of a global IT Market Category (DEX) that is shaping the future of how the world works, giving our customers' IT Teams total digital visibility across their enterprise. Our innovative solutions integrate real-time analytics, automation, and employee feedback across all endpoints. This enables our IT teams to solve complex technical challenges, create ever more productive workplaces, and deliver happy, satisfied employees in the digital workplace. With over 1000 employees across 5 continents, Nexthink operates as One Team, connecting, collaborating and innovating to continuously grow. We call our employees 'Nexthinkers' and our commitment to diversity, inclusion, and equity is second to none. We currently have over 75 nationalities working with us, from all cultures and backgrounds, speaking many different languages.

• Refactor Neural Network Collaborate with architect and author of neural network bond risk product to identify areas for improvement. • Lead architecture and development effort Ongoing Contribute to the design, development, and deployment of firm-wide architecture, norms, policies, infrastructure and methodologies for machine learning activities across multiple company groups. • Design, develop, and deploy machine learning models into production environments. • Collaborate with data scientists to translate prototypes into production-ready systems. • Build and maintain data pipelines, feature stores, and model-serving infrastructure. • Evaluate and optimize model performance, latency, and scalability. • Implement automated training, testing, and deployment workflows (MLOps). • Monitor models in production and address issues related to drift, performance degradation, or data quality. • Conduct code reviews and ensure best practices in ML engineering and software development. • Stay current with emerging ML/AI technologies and recommend tools or frameworks that improve team efficiency.

• Design, build, and maintain scalable data pipelines and workflows to support analytics, fraud detection, model development, and ongoing data monitoring (e.g., using Spark, Airflow, or similar distributed systems). • Leverage and build agentic AI and LLM-powered systems to automate data exploration, anomaly detection, vendor evaluation, and investigative workflows, increasing the speed and depth of insight generation. • Build and optimize models using a variety of input data types, including tabular data, natural language, point clouds, and images, in support of fraud detection and identity verification use cases. • Own data quality and integrity for critical datasets, implementing monitoring, validation checks, and anomaly detection to ensure reliable input to models and downstream decision systems. • Take ownership of project outcomes from scoping through delivery, managing data quality, technical trade-offs, and timelines; proactively escalate risks and work cross-functionally to resolve challenges. • Evaluate and integrate third-party data vendors and external datasets, including designing experiments to assess data quality, coverage, lift, and long-term value for Socure’s models and products. • Collaborate closely with Product, Engineering, and Risk teams to define data requirements, shape roadmap priorities, and deliver insights that guide strategic decisions for fraud and identity products. • Conduct in-depth research to explore new data sources and develop novel algorithms and features that advance the state of the art in fraud detection, identity resolution, and risk scoring. • Lead the end-to-end ML/analytics lifecycle for assigned projects: problem definition, data exploration, feature engineering, modeling, evaluation, deployment handoff, and post-deployment monitoring where applicable. • Present findings, trade-offs, and recommendations to technical and executive stakeholders with clarity and influence, adapting communication for audiences ranging from engineers to non-technical business leaders. • Mentor and share knowledge with peers and junior data scientists, fostering a culture of experimentation, rapid iteration, and continuous learning aligned to Socure’s leadership competencies. • Stay current with advancements in AI, machine learning, and data infrastructure (including LLMs and agentic frameworks), and apply innovative techniques to real-world fraud and identity problems. • Model Socure’s embedded leadership competencies in day-to-day work: continuous learning, effective communication, accountability, team development, decision making, and managing change.