Pipeline: AI/ML Supply Chain Data Scientist

BlockTXM Inc.; Colorado; On-site; USD 120,000 - 170,000; Full time

Job summary

BlockTXM Inc. in Colorado is seeking an AI/ML Supply Chain Data Scientist to apply machine learning and analytics to forecast demand, optimize routes and inventory, and improve network efficiency.

You will build data pipelines from ERP, WMS and IoT sources, develop forecasting and optimization models, and present insights to cross-functional teams to influence decisions. Collaboration with logistics, production, and procurement is essential.

Qualifications

• Strong knowledge of data science and ML applied to supply chain problems.
• Proficiency in Python and SQL for data processing and modeling.
• Experience with time-series forecasting and optimization methods.
• Ability to translate complex analyses into actionable business recommendations.
• Excellent cross-functional collaboration and communication skills.

Responsibilities

• Analyze large datasets of supply chain information to identify patterns and outliers.
• Develop forecasting models for demand planning and lead times.
• Design optimization algorithms for routing, inventory placement, and network design to reduce costs.
• Use predictive analytics to anticipate delays or stockouts.
• Collaborate with logistics, production, and procurement to align analytics with operations.
• Build data pipelines from ERP, WMS, and IoT sources for analysis.
• Develop dashboards to deliver real-time insights to stakeholders.
• Validate and back-test models to measure impact.

Skills

Data Science

Machine Learning

Python

R

Predictive Analytics

Forecasting

Optimization

SQL

Big Data

Time-Series Analysis

TensorFlow/PyTorch

Data Visualization

Cloud ML

Tools

Python (Pandas/Scikit-learn)

R

SQL

TensorFlow/PyTorch

Hadoop/Spark

Data Visualization tools

Job description

AI/ML Supply Chain Data Scientist Description

A Supply Chain Data Scientist applies machine learning and advanced analytics to solve complex supply chain problems. This role, often full-time in larger companies or consulting for specific projects, leverages data to forecast demand, optimize routes and inventory, and improve overall network efficiency. With the rise of AI in logistics, Ohio firms (retailers, manufacturers) are seeking talent who can turn big data (from sales, production, IoT sensors) into predictive insights and competitive advantage.

Responsibilities

• Analyze large datasets of supply chain information (e.g., historical sales, inventory levels, shipment transit times) to identify patterns, trends, and outliers affecting performance.


• Develop and refine forecasting models for demand planning, lead times, or supply, using techniques like time-series analysis or machine learning regression to improve accuracy over traditional methods.


• Design and implement optimization algorithms (operations research or AI-based) for routing, network design, inventory placement, and resource allocation that minimize cost or maximize service level.


• Use predictive analytics to anticipate issues such as delays, stockouts, or quality problems – e.g., predicting which shipments might arrive late or which SKUs are at risk of going out-of-stock.


• Collaborate with cross-functional teams (logistics, production, procurement) to ensure models and analytics align with operational realities and constraints. (For example, incorporate warehouse capacity or carrier schedules into optimization models.)


• Build data pipelines and tools (with help of data engineers) to gather data from various sources (ERP, WMS, IoT sensors, external market data) and preprocess it for analysis (cleaning, feature engineering).


• Develop dashboards or applications to deliver the insights to stakeholders in real-time – for example, a control tower view that uses machine learning outputs to flag anomalies or recommend actions.


• Validate and back-test models; measure the accuracy and business impact of data science initiatives (e.g., how much inventory reduction was achieved or how much service level improved due to the model).


• Present findings and complex analyses in a clear, concise manner to non-technical stakeholders, translating data science results into business recommendations (e.g., \"reducing safety stock by 10% in these warehouses based on our model’s insights\").


• Stay current on AI/ML trends and tools (like advanced neural networks for demand sensing, or reinforcement learning for dynamic routing) and experiment with their applicability to supply chain challenges.


• Support broader digital transformation efforts, possibly guiding junior analysts or influencing IT on infrastructure needed (cloud computing, big data platforms) for advanced analytics.

Requirements Skill Tags

Data Science, Machine Learning, Python (Pandas/Scikit-learn), R, Predictive Analytics, Forecasting, Optimization, SQL, Big Data (Hadoop/Spark), Statistics, Supply Chain Modeling, Linear Programming, Time-Series Analysis, TensorFlow/PyTorch (if deep learning), Data Visualization, Operations Research, Algorithm Development, Cloud ML (AWS SageMaker/Azure ML), Data Cleaning, IoT Data, Demand Planning, Logistics Optimization, Problem Solving, Business Communication, Cross-Functional Collaboration, Domain Knowledge (Supply Chain).