Senior Data Scientist II

At Talkdesk, we are courageous innovators focused on redefining the customer experience, making the impossible possible for companies globally. We champion an inclusive and diverse culture representative of the communities in which we live and serve. And, we give back to our community by volunteering our time, supporting non-profits, and minimizing our global footprint. Each day, thousands of employees, customers, and partners all over the world trust Talkdesk to deliver a better way to great experiences. We are recognized as a cloud contact center leader by many of the most influential research organizations, including Gartner and Forrester. With $498 million in total funding, a valuation of more than $10 Billion, and a ranking of #16 on the Forbes Cloud 100 list, now is the time to be part of the Talkdesk legacy to help accelerate our success in a new decade of transformational growth. At Talkdesk, we embrace FAST, our fundamental operating principles that define who we are as an organization. These principles drive us to make the impossible possible. FAST: Focus + Accountability + Speed = Talkdesker. Focus: Focus time, energy and attention on what is most impactful for the business and thoughtful about how and when to partner with others. Accountability: Hold self and others accountable to meet commitments and drive results. Accept responsibility for successes and failures. Speed: Execute with agility and urgency. Act promptly, decisively, and without delay. Make good and timely decisions that keep the organization moving forward. Talkdesker: YOU! Role Summary: We are looking for a highly technical AI Engineer to join our People Team and accelerate our journey to become an AI-first organization. In this role, you’ll be at the heart of transforming how our People function operates by taking repetitive, manual processes and replacing them with AI-powered, scalable solutions. Your work will directly improve how thousands of employees experience their careers here, while showcasing what it means to run a truly AI-driven People function. As a mid-level individual contributor, you will design and maintain the data architecture, pipelines, and integrations that consolidate people data from multiple systems into a centralized analytics environment. In your first year, your priority will be building a robust data foundation — ensuring clean, well-structured, reliable data that powers analytics and future AI applications. From there, you’ll expand into automation and machine learning solutions — for example, predictive models or AI-driven tools that elevate HR decision-making. This role is highly collaborative, partnering with the People Analytics team, IT data engineers, PBPs, and external vendors or consultants to bring advanced technology to life within the People function. Key Responsibilities: Data Pipeline Development: Build and manage scalable data pipelines to extract, transform, and load (ETL/ELT) people data from all our HR systems (Workday, Greenhouse, Peakon, Workramp, etc.) into a central data repository (Snowflake). Ensure data from different sources is properly connected (e.g. linking recruiting data to employee records) to enable holistic analysis. For example, you will design and develop people analytics data models and pipelines that provide efficient reporting across global HR stakeholders. This includes scheduling and orchestrating workflows, writing efficient scripts to transform data, and embedding data validation checks and alerting for data quality. Data Architecture & Modeling: Define the architecture for our People Analytics data environment. Structure a data warehouse or data lake that organizes HR data (e.g. employee demographic data, recruitment funnel data, performance scores, engagement survey results) in a logical, query-friendly manner. Develop and maintain dimensional data models and tables that support analytics needs (e.g. fact tables for headcount, snapshots for historical trend analysis). Ensure that the data architecture can scale with growth and accommodate new data sources or changes in HR processes. Data Integration & Quality: Work closely with IT and system owners to implement data integration solutions (APIs, scheduled exports, etc.). Monitor data flows to ensure timely and accurate updates. Implement robust data quality controls – for instance, building validation rules, anomaly detection, and notifications when data is incomplete or inconsistent. The goal is to deliver a reliable dataset for analysis with minimal manual intervention. Data quality and scalability with minimal manual work (DevOps) should be a hallmark of your solutions. Analytics & Reporting Enablement: Collaborate with People Analyst(s) to understand their data needs and optimize data structures for reporting (e.g. in Looker). Create documentation and maintain definitions for the data (e.g. data dictionary) to ensure consistency. Where helpful, develop automated data views or queries that analysts and People partners can use for self-service reporting. You may also build or support the enhancement of data visualizations and dashboards in our BI tools to surface key metrics. Automation of HR Processes: Identify opportunities to streamline, enable self-service, and automate manual processes in the People Ops realm. This could include building scripts or small applications to automate data transfers between systems (for example, automating a daily sync of new hires from Workday to downstream systems), generating routine reports or audit logs, or using robotic process automation (RPA) or scripts to eliminate repetitive administrative tasks. Work with HR process owners to prioritize automations that save time and reduce errors. AI and Machine Learning Projects: As the data foundation matures, lead experimentation with Ai/ML solutions to address HR needs. Develop predictive models or algorithms on people data (for example, flight risk predictions, quality of hire, performance prediction, s

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