• Design, develop and refine machine learning models ranging from linear and logistic regression through to tree-based methods, ensemble approaches and advanced algorithms • Develop proof of concepts and translate exploratory insights into structured modelling approaches • Design and execute statistical offline simulations to validate modelling approaches prior to live experimentation • Plan, implement and analyse controlled A/B tests, ensuring statistical robustness and clear interpretation of results • Define success metrics aligned to business objectives and ensure appropriate evaluation frameworks are in place • Analyse model performance using appropriate metrics such as precision, recall, ROC-AUC, log loss, calibration measures and model-specific metrics • Identify potential sources of bias, data leakage and experimental confounding, and proactively mitigate associated risks • Work closely with Product, Engineering and other stakeholders to prioritise opportunities and ensure models deliver measurable value • Communicate complex modelling concepts, trade-offs and experimental results in a clear and accessible way to both technical and non-technical audiences • Contribute to raising the overall standard of modelling practices within the team

• Diseñar, evaluar y monitorear agentes inteligentes y modelos de Machine Learning / Generative AI • Desarrollar modelos supervisados y no supervisados usando Python • Construir y mantener pipelines de experimentación, flujos de validación y procesos de implementación • Operar modelos de producción a través de Azure Machine Learning

Role Description This role offers a unique opportunity to lead advanced data science initiatives in the manufacturing domain, driving measurable improvements across production, operations, and supply chain processes. You will work on end-to-end data solutions, from integrating industrial systems and extracting real-time data to building predictive models and delivering actionable insights. The position blends hands-on engineering with strategic problem solving, allowing you to influence operational efficiency and decision-making at scale. Ideal candidates are skilled in cloud-based data pipelines, machine learning, and industrial protocols, and can communicate complex analyses effectively to both technical teams and executive stakeholders. You will collaborate across remote, global teams in a dynamic, innovative environment that values impact, learning, and cross-functional expertise. - Lead the development and deployment of predictive models for maintenance, anomaly detection, demand forecasting, and process optimization in manufacturing settings - Build and optimize scalable cloud data pipelines for high-volume IoT and operational data using tools like Spark, Kafka, Airflow, and Delta Lake - Integrate OT and IT systems including MES, SCADA, and ERP for real-time data extraction and analysis using industrial protocols such as OPC-UA, MQTT, and Modbus - Apply statistical methods, Six Sigma, SPC, OEE, and lean methodologies to drive measurable gains in yield, uptime, and efficiency - Translate complex model outputs into actionable insights and recommendations for operations and executive stakeholders - Design and execute experiments and simulations to validate process improvements and quantify business impact - Collaborate with cross-functional teams to ensure solutions align with business objectives and operational realities Qualifications - 8–10 years of overall data science experience with 2–4 years in the manufacturing domain - Strong proficiency in SQL and Python, with hands-on experience in medallion/lakehouse architectures on platforms like Databricks, Snowflake, AWS, or Azure - Proven experience in building, deploying, and operationalizing ML models in industrial environments using scikit-learn, TensorFlow, or PyTorch - Deep understanding of industrial operations, production planning, and systems (MES, SCADA, ERP) - Expertise in data engineering, cloud data pipelines, and IoT data integration - Solid grounding in statistical methods: time series analysis, regression, clustering, hypothesis testing - Strong communication skills with the ability to convey technical insights to non-technical stakeholders - Experience designing A/B experiments and process simulations to evaluate business impact - Adaptable, collaborative, and capable of working in a fast-paced, remote, and cross-functional environment Benefits - Opportunity to work on cutting-edge data science projects in manufacturing and industrial operations - Remote work with occasional travel across the United States - Exposure to high-impact, enterprise-scale industrial data environments - Collaborative, innovative, and non-hierarchical work culture - Professional growth opportunities through hands-on experience with advanced ML and data engineering - Competitive contract-to-hire compensation

Role Description The Computer Scientist (Journeyman) supports the research, design, and development of software-based analytical solutions. This role focuses on applying computer and information science principles to solve complex data, modeling, or system challenges. Qualifications - Bachelor’s degree (BA/BS) in Computer Science or a related field - 3–15 years of professional experience in software development, systems engineering, or applied computer science Requirements - U.S. citizenship - Ability to obtain and maintain a Tier 3 investigation - Experience working in secure or regulated environments preferred Benefits - Health insurance - Dental insurance - Vision insurance - Life Insurance - 401(k) Retirement Plan with matching - Paid Time Off - Paid Federal Holidays