Company Overview
[$COMPANY_OVERVIEW]
Role Overview
[$COMPANY_NAME] is seeking a Senior Time Study Technologist to lead advanced work measurement, time-motion analysis, and operational research initiatives that drive productivity, safety, and cost-to-serve optimization. In this senior role you will architect and operationalize robust time-study programs across multiple sites, integrate digital data capture and computer vision workflows, and translate quantitative findings into engineering-ready requirements, SOPs, and capacity models. You will partner with operations leadership, industrial engineering, UX/ergonomics, and data science to deliver measurable throughput and quality improvements.
Responsibilities
- Design, lead and own end-to-end time study programs: protocol design, sampling strategy, observer training, data capture (manual and digital), analysis, and reporting across distribution, manufacturing, or service environments.
- Architect and deploy digital time-capture solutions including high-frame-rate video, wearable sensors (IMUs), barcode/RFID triggers, and ergonomics assessment tools; integrate with data pipelines and databases for downstream analytics.
- Develop and validate automated motion and activity recognition workflows using computer vision toolkits (OpenCV, MediaPipe) and machine learning inference where appropriate; establish accuracy and reliability metrics.
- Create statistically sound sampling plans (stratified, systematic, cluster), compute confidence intervals and required sample sizes, and quantify measurement error and bias for continuous improvement initiatives.
- Analyze large time-and-motion datasets using Python/R and SQL to extract cycle times, takt time, utilization, variability, bottlenecks and value/non-value-added activities; produce actionable recommendations with estimated business impact.
- Lead cross-functional workshops to translate time-study results into operational changes: capacity planning, line balancing, layout changes, staffing models, SOP updates, and tooling investments.
- Mentor and train a distributed team of technicians and junior analysts on observer reliability, data collection standards, measurement system analysis (MSA), and field tooling.
- Define KPIs and dashboards (Power BI, Looker, Tableau) to monitor changes over time; partner with analytics/BI to automate recurring reports and alerts.
- Maintain rigorous data governance and privacy practices for video and sensor data; manage consent workflows and secure storage in accordance with company policy and applicable law.
- Author technical documentation, standard operating procedures, and Architecture Decision Records (ADRs) for measurement systems and instrumentation choices.
Required Qualifications
- Bachelor’s degree in Industrial Engineering, Manufacturing Engineering, Human Factors, Operations Research, Applied Statistics or a closely related technical field; Master’s preferred.
- 6+ years of progressive experience designing and running time studies, work measurement, time-and-motion analysis, or industrial engineering projects in high-throughput operational environments.
- Proven expertise with digital data capture methods: high-resolution video, wearable sensors, barcode/RFID, and synchronized multi-source logging.
- Strong programming and analysis skills: Python (pandas, numpy), R, SQL; experience building reproducible analysis pipelines and unit-tested code for data processing.
- Demonstrated ability to design statistically valid sampling plans, compute sample sizes, assess inter-rater reliability, and perform measurement system analysis.
- Experience deploying computer vision or rule-based activity recognition workflows in production or near-production environments.
- Track record of delivering measurable operational impact (e.g., throughput increase, takt time reduction, FTE reallocation) and building business cases for improvements.
- Excellent written and verbal technical communication skills; experience presenting to senior leadership and translating technical results to operational decision-makers.
Preferred Qualifications
- MS or higher in a quantitative field (Industrial Engineering, Statistics, Human Factors, Computer Vision).
- Experience with annotation tools (Labelbox, CVAT), ML model evaluation metrics (precision/recall/ROC), and lightweight model deployment (TensorFlow Lite, ONNX Runtime).
- Familiarity with cloud data platforms (AWS S3, Athena, Redshift, GCP BigQuery) and containerized workflows (Docker, Kubernetes) for reproducible processing.
- Experience with process improvement methodologies: Lean, Six Sigma (Green/Black Belt), 5S, Kaizen—able to lead DMAIC projects end-to-end.
- Experience integrating time-study outputs into workforce management and scheduling systems (Kronos/Workday/UKG/etc.).
Technical Skills and Relevant Technologies
- Programming: Python (pandas, numpy), R, SQL; version control with Git; CI/CD basics for analytics pipelines.
- Computer Vision & Sensing: OpenCV, MediaPipe, basic ML inference frameworks, IMU data processing, synchronization of multi-modal signals.
- Data & BI: ETL best practices, relational and columnar databases, Power BI/Looker/Tableau dashboarding.
- Statistical Methods: sampling design, hypothesis testing, ANOVA, confidence intervals, MSA, reliability statistics (Cohen's kappa, ICC).
- Operational Tools: barcode/RFID systems, high-speed video capture, wearable sensors, ergonomic assessment tools (REBA, RULA).
- Process Improvement: Lean/Six Sigma methodologies, value stream mapping, capacity planning and line balancing tools.
Soft Skills and Cultural Fit
- Leadership: history of leading cross-functional initiatives, influencing without authority, and driving measurable change.
- Coaching: proven experience mentoring technicians and analysts on rigorous data collection and analysis practices.
- Analytical Communication: ability to craft concise, data-driven recommendations and present ROI to senior stakeholders.
- Attention to Detail: meticulous about measurement validity, timestamp synchronization, and documentation.
- Bias for Action: comfortable designing pragmatic studies under operational constraints and iterating quickly based on pilot results.
- Inclusive collaborator: committed to building diverse teams and incorporating frontline operator feedback into technical solutions.
Benefits and Perks
Annual salary range: [$SALARY_RANGE]
- Comprehensive medical, dental, and vision insurance
- 401(k) or local equivalent with company match
- Generous paid time off, parental leave, and flexible scheduling
- Professional development stipend and certification reimbursement (Six Sigma, Lean, data science courses)
- Relocation assistance for eligible candidates
- Equipment allowance for field data capture devices and remote analysis workstation
Equal Opportunity Statement
[$COMPANY_NAME] is an equal opportunity employer and is committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender identity or expression, sexual orientation, national origin, age, disability, veteran status, or any other characteristic protected by applicable law. We encourage applicants from underrepresented groups to apply.
Location
This role is primarily on-site at [$COMPANY_LOCATION] with occasional travel between sites and to vendor locations. Candidates must be able to perform field data collection and site-based observations as required.
Application Instructions
We encourage applicants with diverse backgrounds and experiences to apply even if your profile does not meet every listed qualification. Please submit a resume and brief cover letter highlighting relevant time-study programs, tooling you have implemented, and a short description of an operational impact you delivered.