Role Overview:

• Lead the design, development, training, and deployment of machine learning and AI models to process, analyze, and derive insights from field equipment sensor data (e.g., time-series IoT, embedded device telemetry) alongside structured/unstructured datasets.
• Build and optimize predictive, prescriptive, and anomaly detection models using advanced ML techniques (e.g., regression, time-series forecasting, deep learning) to enable real-time or near-real-time decision-making from sensor streams and other data sources.
• Integrate AI into end-to-end workflows, including automated data ingestion, feature engineering, model training pipelines, inference at the edge or cloud, and continuous monitoring for drift/performance degradation.
• Perform exploratory data analysis (EDA), preprocessing, and feature extraction on high-volume, noisy sensor data and multimodal natural datasets to uncover patterns, correlations, and actionable insights.
• Collaborate with engineering, product, and domain experts to translate business problems (e.g., predictive maintenance, fault detection, optimization) into data science/AI solutions that deliver measurable impact.
• Apply statistical modeling, causal inference, and experimentation (A/B testing, hypothesis testing) to validate models and ensure robustness in dynamic, real-world environments involving sensor and operational data.
• Champion AI-driven innovation by exploring emerging techniques (e.g., generative AI for synthetic sensor data, edge AI optimization, multimodal fusion) and incorporating them into production workflows.
• Establish reproducible, scalable ML pipelines (MLOps practices) for model versioning, retraining, deployment (including edge/embedded constraints), and lifecycle management.
• Communicate complex technical findings, model performance, and business value to stakeholders through clear visualizations, reports, and presentations to drive data-informed strategic decisions.
• Responsible for formulating, suggesting, and managing data-driven project requirements against business needs related to strategic company goals.
• Data cleansing and collation to ensure models are both accurate and reliable.
• Solve complex technical challenges focused on analytical tool sets for decision making.
• Assume responsibility for subject matter expertise for analytical tools on cross functional product development teams.
• Work closely with software and business development teams to maximize revenue opportunities associated with Data Science initiatives and enhancement of products and services and related to data interpretation.
• Assist and support internal resources involved in research, product development, and ongoing production of data analytics.

• 8+ years of experience as Data Scientist or similar role
• 8+ years of professional experience in data science/ML roles, with at least 5 years focused on AI/ML modeling for time-series/sensor/IoT data or similar high-velocity, real-world datasets.
• Minimum of Bachelor of Science in Mathematics, Applied Statistics, Data Science Process Engineering, or Computer Science
• Proficiency in Python, Mathematica, or similar
• Advanced statistical modeling, hypothesis testing, causal inference, experimental design (A/B testing), and evaluation metrics for robust, interpretable models
• Proficiency in C, C+, C++, C#, Java, a variety of data bases such as SQL, MongoDB,
• Deep experience building, training, fine tuning and deployment of machine learning and deep learning models using frameworks such as PyTorch
• Hands-on experience integrating AI into end-to-end workflows, including feature engineering, modeling training pipelines, inference (cloud and edge) and real time/near real time applications
• Excellent problem-solving, critical thinking, and ability to translate complex technical concepts into business impact and stakeholder recommendations.

• Masters in Applied Mathematics, Applied Statistics, or Data Science
• Experience with Machine health data sets
• Expertise in domain-relevant applications such as predictive maintenance, fault detection, optimization, and condition monitoring using ML on sensor streams.
• Proven ability to handle high-volume, noisy, streaming sensor data (IoT telemetry, embedded systems, industrial sensors) including preprocessing, feature extraction from time-series/multimodal data, and handling issues like missing values, drift, or irregularity.
• Experience in the Industrial industry, mainly Power or Oil and Gas

• Candidate will be responsible for reviewing policies and procedures related to cybersecurity and those relevant to the functions of their role.
• Candidate is expected to maintain a cybersecure work environment.

• Must be able to sit and stand for extended periods of time.
• Must be able to use hands to type, handle products, tools and navigate a computer keyboard.
• Must be able to view computer screen for extended periods of time.
• Specific vision abilities required by this job include close vision and distance vision

• 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
• All other benefits required by applicable law