Senior Data Scientist – Industrial Focus

Role Overview

• Design, develop, train, and deploy machine learning and AI models that process and analyze field equipment sensor data (time-series IoT, embedded device telemetry) alongside structured and unstructured datasets.
• Build and refine predictive, prescriptive, and anomaly detection models using techniques such as regression, time-series forecasting, classification, clustering, and deep learning to support real-time or near-real-time decision-making.
• Perform exploratory data analysis (EDA), data preprocessing, feature engineering/signal processing, and feature extraction on high-volume, noisy sensor data and multimodal datasets to surface patterns, correlations, and actionable insights.
• Contribute to end-to-end AI workflows, including automated data ingestion, model training pipelines, inference at the edge or in the cloud, and continuous monitoring for model drift and performance degradation.
• Apply statistical modeling, hypothesis testing, and experimentation methods (A/B testing, causal inference where applicable) to validate model performance and ensure robustness in dynamic operational environments.
• Support the development and maintenance of reproducible, scalable ML pipelines using MLOps best practices, including model versioning, retraining, deployment (including edge/embedded constraints), and lifecycle management.
• Collaborate with engineering, product, and domain experts to translate business problems (e.g., predictive maintenance, fault detection, process optimization) into well-defined data science solutions.
• Perform data cleansing, validation, and collation activities to ensure models are accurate, reliable, and aligned with real-world operating conditions.
• Solve complex technical challenges related to analytical toolsets that support engineering and operational decision-making.
• Communicate technical findings, model performance metrics, and business value to internal stakeholders through clear visualizations, written reports, and presentations.
• Explore and evaluate emerging techniques (e.g., generative AI for synthetic sensor data, edge AI optimization, multimodal data fusion) and recommend incorporation into production workflows where appropriate.
• Assist in formulating and managing data-driven project requirements aligned with business needs and strategic company goals.
• Provide subject matter input on analytical tools and methods to cross-functional product development teams.
• Work with software and business development teams to support revenue opportunities tied to data science initiatives and product/service enhancements.
• Support internal resources involved in research, product development, and ongoing production of data analytics deliverables.

Requirements

• Bachelor's degree in Engineering required; Mechanical, Electrical, Chemical, or Aerospace strongly preferred.
• Formal training or demonstrated proficiency in data science, machine learning, and applied analytics required.
• 5+ years of professional experience in data science, machine learning, signal processing, and applied analytics; Master’s or PhD in a relevant field may substitute for up to 2 years of required experience.
• Direct industry experience required in one or more of the following sectors: Power Generation, Oil & Gas, Aerospace, Pulp & Paper, Manufacturing, or similar industries.
• Demonstrated experience working with time-series data, sensor data, and operational/IoT data within an industrial environment.
• Has independently owned at least one ML model from prototype through production, including monitoring and retraining in a live environment.
• Experience supporting use cases such as predictive maintenance, fault/anomaly detection, asset health monitoring, or process optimization.
• Proficiency in Python (NumPy, pandas, scikit-learn, TensorFlow/PyTorch), SQL, time-series databases (InfluxDB, TimescaleDB, Snowflake), and visualization tools (Power BI, Tableau, Plotly).
• Hands-on experience with time-series modeling techniques (e.g., ARIMA, Prophet, LSTMs, transformers for sequence data).
• Practical experience with anomaly detection methods on streaming or batch sensor data.
• Familiarity with cloud platforms (AWS, Azure, GCP) and MLOps practices including MLflow, Airflow, Docker, and CI/CD pipelines.
• Strong analytical and problem-solving skills with attention to detail.
• Excellent written and verbal communication skills, with the ability to present complex findings to non-technical audiences.
• Effective collaborator across engineering, product, and business teams.
• Self-motivated and capable of managing multiple priorities in a fast-paced environment.
• Active contributes to the broader data science and industrial AI community through open-source projects, technical publications, conference presentations, or patents; a track record of knowledge sharing is valued and supported.

Tech Stack

• Airflow
• AWS
• Azure
• Cloud
• Docker
• Google Cloud Platform
• IoT
• Numpy
• Pandas
• Python
• PyTorch
• Scikit-Learn
• SQL
• Tableau
• Tensorflow

Benefits

• Paid Time Off
• Medical, Vision, Dental Insurance
• Health Savings Account with Employer contributions
• 401(k) with Employer match
• Short-term & Long-term Disability Coverage
• Accidental Death & Dismemberment Coverage
• Life Insurance Coverage
• Eight paid holidays per year