Data Scientist – Predictive Modeling & Supply Chain Analytics

At Lumenalta, we partner with forward-thinking organizations to build technology solutions that scale, delight users, and accelerate business growth. Our global teams bring curiosity, commitment, and technical excellence to every project. We value transparency, autonomy, and impact—empowering every team member to do their best work. We’re seeking an experienced Data Scientist with a strong applied statistics background and deep supply chain domain knowledge to join an enterprise manufacturing client. Actively Hiring We are hiring for a current opening on an active client project. This is a specific, presently open role. We review applications on a rolling basis and aim to move qualified candidates through our process promptly. What You’ll Work On Build demand forecasting models using time-series methods appropriate to supply chain variability—applying CV-based segmentation to classify materials by demand pattern and selecting the right forecasting approach per segment (e.g., smooth, erratic, lumpy, intermittent). Perform demand variability analysis across material master and movement data—quantifying forecast error, identifying root causes of variability, and producing inputs that planning teams can act on directly in SAP. Develop inventory pre-positioning and health/trend monitoring (HTMS) analytics, translating model outputs into actionable signals for supply chain planners—including safety stock recommendations, reorder point adjustments, and MRP parameter tuning. Work directly with SAP ECC planning data—extracting and interpreting MD07 exception messages, purchase requisitions vs. PO receipts, and MRP-generated signals to understand how planners currently work and where predictive models create the most value. Partner with cross-functional teams—supply chain planners, procurement, and operations—to translate domain knowledge into concrete model inputs, validate outputs against real planning decisions, and communicate findings in terms the business understands. Perform exploratory data analysis (EDA) on SAP transactional data (MARC, MARD, EKKO/EKPO, purchase reqs) to uncover demand patterns, supply variability, and leading indicators that inform forecasting and inventory models. Build and maintain analytical workflows in Databricks using PySpark, SQL, and Notebooks—ensuring reproducibility, version control, and handoff-readiness for engineering and downstream consumers. Establish modeling best practices for validation, segmentation logic, and presentation of findings—ensuring models are interpretable and trusted by both technical teams and supply chain planners who act on the outputs. Requirements 5+ years in a Data Scientist or analytical role, with hands-on experience building statistical models in supply chain, manufacturing, or industrial planning contexts. Strong foundation in applied statistics—time-series forecasting fundamentals (ARIMA, exponential smoothing, intermittent demand models), demand variability analysis, and CV-based material segmentation. Classical statistical rigor is valued over black-box ML here. Working understanding of supply chain planning concepts—MRP logic, purchase requisitions vs. PO receipts, safety stock, reorder points, and how planners make decisions—sufficient to translate planning requirements into model design. Hands-on experience with Databricks for analytical model development—PySpark and Spark SQL for data transformation and aggregation, and Notebooks for reproducible analysis and model iteration. Strong proficiency in Python (scikit-learn, statsmodels, pandas) for modeling and SQL for extracting and manipulating large SAP transactional datasets. Ability to present model outputs and analytical findings to both technical teams and non-technical supply chain stakeholders—translating statistical outputs into planning-relevant language. Strong written and verbal communication skills in English. Why Lumenalta is an amazing place to work at At Lumenalta, you can expect that you will: Be 100% dedicated to one project at a time so that you can innovate and grow. Be a part of a team of talented and friendly senior-level developers. Work on projects that allow you to use leading tech. Salary Salary range: CA$80,000 - CA$130,000 annually, with final compensation determined by your qualifications, expertise, experience, and the role's scope. Location: This is a fully remote position; however, candidates must be based in regions that align with the Pacific, Central, or Eastern U.S. time zones to ensure effective collaboration with client and team schedules. Occasional travel to Georgia will be required. Benefits In addition to competitive pay, we offer a variety of benefits to support your professional and personal growth, including: Flexible working hours in a remote environment. Health insurance (medical and dental) for T4 Employees. A professional development fund to enhance your skills and knowledge. 15 days of paid time off annually. Access to soft-skill development courses to further your career. Position Details This is a full-time position requiring a minimum of 40 hours per week, Monday through Friday. Application Deadline Applications will be accepted until July 26, 2026. Candidates can expect feedback by August 3, 2026.

The Data Scientist will build demand forecasting models and perform demand variability analysis to support supply chain planning. They will also develop analytics for inventory management and work directly with SAP data to enhance planning processes.

Candidates should have over 5 years of experience in data science with a strong foundation in applied statistics and supply chain knowledge. Proficiency in tools like Databricks, Python, and SQL is essential for this role.

• Flexible Working Hours • Health Insurance • Professional Development Fund • Paid Time Off • Access to Soft-Skill Development Courses

Identified from the job description. Confirm important requirements above.

• Data Science
• Predictive Modeling
• Supply Chain Analytics
• Time-Series Forecasting
• Statistical Modeling
• Demand Variability Analysis
• SAP
• Databricks
• PySpark
• SQL
• Python
• Scikit-Learn
• Statsmodels
• Pandas
• Exploratory Data Analysis
• Communication

Minimum experience

5+ years