Control Tower Data Intelligence

Job Summary

This individual contributor is primarily responsible for designing and developing data pipelines and automation for data acquisition and ingestion of raw data from multiple data sources and data formats by transforming, cleansing, and storing data for consumption. This role is also responsible for developing detailed problem statements outlining hypotheses and their effect on target clients/customers, analyzing and investigating complex data sets and summarizing key characteristics, selecting, manipulating and transforming data into features used in machine learning algorithms, training statistical models, deploying and maintaining reliable and efficient models through production, verifying model performance, and collaborating with internal and external stakeholders across domains to develop and deliver statistical driven outcomes.

Essential Responsibilities

• Promotes learning in others by proactively providing and/or developing information, resources, advice, and expertise with coworkers and members; builds relationships with cross-functional/external stakeholders and customers. Listens to, seeks, and addresses performance feedback; proactively provides actionable feedback to others and to managers. Pursues self-development; creates and executes plans to capitalize on strengths and develop weaknesses; leads by influencing others through technical explanations and examples and provides options and recommendations. Adopts new responsibilities; adapts to and learns from change, challenges, and feedback; demonstrates flexibility in approaches to work; champions change and helps others adapt to new tasks and processes. Facilitate team collaboration to support a business outcome.
• Completes work assignments autonomously and supports business-specific projects by applying expertise in subject area and business knowledge to generate creative solutions; encourages team members to adapt to and follow all procedures and policies. Collaborates cross-functionally and/or externally to achieve effective business decisions; provides recommendations and solves complex problems; escalates high-priority issues or risks, as appropriate; monitors progress and results. Supports the development of work plans to meet business priorities and deadlines; identifies resources to accomplish priorities and deadlines. Identifies, speaks up, and capitalizes improvement opportunities across teams; uses influence to guide others and engages stakeholders to achieve appropriate solutions.
• Develops detailed problem statements outlining hypotheses and their effect on target clients/customers by defining scope, objectives, outcome statements and metrics.
• Designs and develops data pipelines and automation for data acquisition and ingestion of raw data from multiple data sources and data formats by transforming, cleansing, and storing data for consumption by downstream processes; writing and optimizing diverse SQL queries; and demonstrating advanced knowledge of database fundamentals.
• Analyzes and investigates complex data sets and summarizes key characteristics by employing data visualization methods; and determining how best to manipulate data sources to discover patterns, spot anomalies, test hypotheses, and/or check assumptions.
• Selects, manipulates, and transforms data into features used in machine learning algorithms by leveraging techniques to conduct dimensionality reduction, feature importance, and feature selection.
• Trains statistical models by using algorithms and data mining techniques; testing models with various algorithms to assess the input dataset and related features; and applying techniques to prevent overfitting such as cross-validation.
• Deploys and maintains reliable and efficient models through production.
• Verifies model performance by demonstrating expertise in the practice of a variety of model validation techniques to assess and discriminate the goodness of model fit; and leveraging feedback and output to manage and strengthen model performance.
• Collaborates with internal and external stakeholders across domains to develop and deliver statistical driven outcomes by delivering insights and values from heterogeneous data to investigate complex problems for multiple use cases; driving informed decision-making; and presenting findings to both technical and non-technical audiences.

Job Qualifications

Minimum Qualifications

• Minimum three (3) years’ experience working with Exploratory Data Analysis (EDA) and visualization methods.
• Minimum three (3) years of machine learning and/or algorithmic experience.
• Minimum three (3) years of statistical analysis and modeling experience.
• Minimum three (3) years of programming experience.
• Minimum one (1) year experience in a leadership role with or without direct reports.
• Bachelor’s degree in mathematics, Statistics, Computer Science, Engineering, Economics, Public Health, or related field AND Minimum five (5) years’ experience in data science or a directly related field. Additional equivalent work experience in a directly related field may be substituted for the degree requirement. Advanced degrees may be substituted for the work experience requirements.

Additional Requirements

• Experience working with supply chain, logistics, or healthcare operations data
• Experience applying analytics to inventory optimization, demand forecasting, or replenishment
• Familiarity with ERP or supply chain systems (e.g., SAP, Oracle, Lawson)
• Experience building control tower analytics or centralized visibility solutions
• Two (2) years’ experience designing end-to-end data flows (ingestion, transformation, storage, and consumption layers)
• Two (2) years’ experience developing data pipelines and ETL/ELT processes using SQL, Python, or similar tools
• Experience working with data architecture concepts (data models, schemas, data lakes/warehouses)
• Experience integrating multiple structured and unstructured data sources into scalable data environments
• Experience supporting real-time or near-time data processing
• Two (2) years’ experience developing algorithms or statistical models for business applications (e.g., forecasting, optimization, anomaly detection)
• Experience applying machine learning, optimization, or simulation techniques
• Experience with operations research methods (e.g., network flow, inventory optimization, linear programming)
• Experience designing experiments, validation frameworks, and model performance monitoring
• Experience deploying and maintaining production-grade analytical models or data products
• Experience building decision-support tools, dashboards, or APIs
• Experience implementing model monitoring, versioning, and lifecycle management (MLOps or similar)
• Experience working with cloud platforms (Azure, AWS, GCP)
• Demonstrated ability to independently design, build, and deploy solutions
• Experience troubleshooting and maintaining data pipelines and analytical systems in production
• Ability to operate across full lifecycle: data acquisition → modeling → deployment → support