{"id":1623391,"url":"https://alion.io/job/intouchcx-workforce-data-science-lead","title":"Workforce Data Science Lead","company":{"id":1795301,"name":"IntouchCX","domain":"intouchcx.com","url":"https://alion.io/company/intouchcx","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Dayforce","truth_index":{"grade":"B","score":75,"open_postings":61,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":58,"computed_at":"2026-10-06T05:45:30Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Manila, Philippines"],"countries":["PH"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":24000,"max_usd":54000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":521},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"BigQuery","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Looker","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"Amazon Redshift","optional":true},{"name":"ETL/ELT","optional":true},{"name":"GCP","optional":true},{"name":"Snowflake","optional":true},{"name":"SQL","optional":true}],"status":"live","first_seen_at":"2026-06-12T18:00:00Z","employer_posted_date":"2026-10-01","last_verified_at":"2026-10-06T20:42:52Z","board_verified":true,"closed_at":null,"days_open":116,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":115},"description":"About the Job\n\nThe 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.\n\nThis 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.\n\nAs WFM Data Science Lead, You Will…\nOwn the end-to-end forecasting data lifecycle (ingestion → validation → transformation → modeling).\nDefine standards for raw data structuring, metric definitions, and forecasting readiness.\nDevelop and enhance time series and machine learning models, incorporating seasonality, campaigns, and external drivers.\nPartner with the Data Team to optimize datasets in Google BigQuery and build scalable workflows in Python.\nIntegrate datasets into dashboards (e.g., Looker Studio) for operational visibility.\nEstablish frameworks for data quality checks, anomaly detection, and reconciliation.\nResolve inconsistencies such as double-counted volumes, IVR/SMS overlaps, and abandoned vs. offered logic.\nBuild automated pipelines to flag data gaps, spikes, and irregular patterns.\nDrive adoption of standardized datasets as the single source of truth across teams.\nTranslate raw data into actionable forecasting insights.\nIdentify key drivers of volume fluctuations (day-of-week patterns, campaigns, operational changes).\nSupport Forecasting teams with scenario modeling and data-backed assumptions.\nImprove forecast accuracy, reduce bias, and increase explainability of models.\nWork closely with Forecasting, Operations, Finance, and IT to ensure data consistency and usability.\nAlign on volume definitions, KPIs, and reporting standards across functions.\nPartner with stakeholders to elevate service levels, efficiency, and decision-making.\nAutomate repetitive data preparation, validation, and reporting processes.\nReduce manual workload within the Forecasting team.\nImprove turnaround time for forecasting cycles and ad hoc analyses.\n\nAs WFM Data Science Lead, You Need…\nBachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, or related field\nA master’s degree is a plus.\nExperience in cloud platforms (GCP preferred) is an advantage.\n7+ years in Data Science, Analytics, or Forecasting-related roles.\nStrong background in Workforce Management (WFM), contact center, or BPO environments.\nProven expertise in volume forecasting, time series analysis, and data preprocessing.\nDemonstrated success in developing and maintaining forecasting models and operational dashboards.\nExperience in customer experience, retail operations, or other high-volume environments.\n2–4 years of experience in business intelligence, data analysis, or dashboard development.\nHands-on experience with Looker and SQL-based databases.\nProficiency in Python for data analysis, automation, and integration projects.\nExperience with ETL processes and cloud data warehouses (BigQuery, Snowflake, Redshift) preferred.\nStrong understanding of raw WFM data structures (calls offered, handled, abandoned, AHT, IVR flows).\nSkilled in data cleaning, anomaly detection, and pipeline validation.\nPrior leadership or team lead experience in analytics or workforce management.\nStrong stakeholder management skills with the ability to translate technical insights into business value.\nBackground in forecasting, KPI tracking, or operational analytics is a plus.\nAdvanced proficiency in:\nPython (data analysis, modeling, automation)\nSQL (data extraction and transformation)\nHands-on experience with:\nGoogle BigQuery (large-scale data handling)\nLooker Studio (dashboarding and reporting)","description_format":"text","description_chars":3878,"description_truncated":false,"requirements":{"experience_years_min":7,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial 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