WFM Data Science Lead

About IntouchCX IntouchCX is a global leader in digital customer experience management, back office processing, trust & safety solutions, and AI services. For over 25 years, we’ve scaled with soul, building trusted long-term partnerships and empowering our people to drive positive change. About The Job The WFM Data Science Lead drives the end-to-end data science strategy within Workforce Management, ensuring forecasting accuracy, raw volume data integrity, and scalable analytics solutions that enable operational excellence. This role is an individual contributor, transforming raw data into validated forecasting inputs for advanced modeling. By partnering with Forecasting, Operations, and Data Engineering teams, the WFM Data Science Lead elevates service levels, enhances efficiency, and empowers faster, data-driven decision-making across the organization. As WFM Data Science Lead, You Will… • Own the end-to-end forecasting data lifecycle (ingestion → validation → transformation → modeling). • Define standards for raw data structuring, metric definitions, and forecasting readiness. • Develop and enhance time series and machine learning models, incorporating seasonality, campaigns, and external drivers. • Partner with the Data Team to optimize datasets in Google BigQuery and build scalable workflows in Python. • Integrate datasets into dashboards (e.g., Looker Studio) for operational visibility. • Establish frameworks for data quality checks, anomaly detection, and reconciliation. • Resolve inconsistencies such as double-counted volumes, IVR/SMS overlaps, and abandoned vs. offered logic. • Build automated pipelines to flag data gaps, spikes, and irregular patterns. • Drive adoption of standardized datasets as the single source of truth across teams. • Translate raw data into actionable forecasting insights. • Identify key drivers of volume fluctuations (day-of-week patterns, campaigns, operational changes). • Support Forecasting teams with scenario modeling and data-backed assumptions. • Improve forecast accuracy, reduce bias, and increase explainability of models. • Work closely with Forecasting, Operations, Finance, and IT to ensure data consistency and usability. • Align on volume definitions, KPIs, and reporting standards across functions. • Partner with stakeholders to elevate service levels, efficiency, and decision-making. • Automate repetitive data preparation, validation, and reporting processes. • Reduce manual workload within the Forecasting team. • Improve turnaround time for forecasting cycles and ad hoc analyses. As WFM Data Science Lead, You Need… • Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, or related field • A master’s degree is a plus. • Experience in cloud platforms (GCP preferred) is an advantage. • 7+ years in Data Science, Analytics, or Forecasting-related roles. • Strong background in Workforce Management (WFM), contact center, or BPO environments. • Proven expertise in volume forecasting, time series analysis, and data preprocessing. • Demonstrated success in developing and maintaining forecasting models and operational dashboards. • Experience in customer experience, retail operations, or other high-volume environments. • 2–4 years of experience in business intelligence, data analysis, or dashboard development. • Hands-on experience with Looker and SQL-based databases. • Proficiency in Python for data analysis, automation, and integration projects. • Experience with ETL processes and cloud data warehouses (BigQuery, Snowflake, Redshift) preferred. • Strong understanding of raw WFM data structures (calls offered, handled, abandoned, AHT, IVR flows). • Skilled in data cleaning, anomaly detection, and pipeline validation. • Prior leadership or team lead experience in analytics or workforce management. • Strong stakeholder management skills with the ability to translate technical insights into business value. • Background in forecasting, KPI tracking, or operational analytics is a plus. • Advanced proficiency in: • Python (data analysis, modeling, automation) • SQL (data extraction and transformation) • Hands-on experience with: • Google BigQuery (large-scale data handling) • Looker Studio (dashboarding and reporting)