Senior Data Scientist II job opportunity at Talkdesk.

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Talkdesk Senior Data Scientist II
Experience: 4-years
Pattern: full-time
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Data Science

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degreeBachelor's (B.Sc.)
loacation Bengaluru, India
loacation Bengaluru....India

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. __ 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, skills/career path recommendations, etc.). Use appropriate machine learning libraries or tools to prototype solutions. Given our focus on employee feedback, you might also apply natural language processing (NLP) to analyze open-ended survey comments or other text data for sentiment and themes. Evaluate the feasibility of Ai tools in areas like resume screening, chatbot assistants for HR, or personalized learning recommendations – working closely with the Director to align these projects with strategic goals. (Note: We are open to either building these solutions or integrating third-party Ai vendors, so you will also partner to research and recommend vendor tools where appropriate.) __ #Collaboration & Consulting: Work in tandem with IT data and InfoSec teams to align on data architecture and adhere to company-wide data security practices. Collaborate with the People Analytics Team to understand business questions and ensure the data is structured to answer them. Provide technical expertise in conversations about new HR tech vendors – for example, assessing how our applicant tracking platform could integrate with our warehouse, or how to export data for analysis. Assist in scoping and implementing any Ai/analytics vendor solutions we might purchase, ensuring that data flows and outcomes meet our requirements. __ Continuous Improvement & #Innovation: Stay up-to-date on emerging technologies in data engineering, analytics, and Ai (especially in the HR domain). Introduce best practices for code management, documentation, and reproducibility. Build reusable frameworks and pipelines that can be leveraged by others on the team, thereby accelerating future development. Always be on the lookout for new tools or methods (for instance, improvements in Workday’s data analytics capabilities or new open-source ML tools) that could enhance our People Analytics capabilities.

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Senior Data Scientist II Applicants are expected to have a solid experience in handling Data Science related tasks