Principal Data Scientist

Cutsforth•Ferndale, WA

1d•$173,330 - $201,043•Remote

About The Position

The Principal Data Scientist is responsible for leading the design, development, and deployment of advanced data science and AI-driven solutions that deliver measurable business value. This role combines deep technical expertise in machine learning, artificial intelligence, statistical modeling, and data mining with strong collaboration and strategic insight to solve complex problems across embedded systems, applications, and platforms. The Principal Data Scientist will define project requirements, partner with engineering, product, and business stakeholders to translate objectives into high impact analytics initiatives and serve as the primary subject matter expert in AI and advanced analytics. Leveraging applied mathematics, predictive modeling, and generative AI techniques, this role drives innovation, uncovers actionable insights, and creates new opportunities within strategic accounts and the broader marketplace.

Requirements

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.

Nice To Haves

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

Responsibilities

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.