Senior Data Scientist

Premium Guard Inc. (PGI)

. Develop, validate, deploy, and monitor machine learning models for forecasting, optimization, customer intelligence, operational decision-making, and business growth .

Posted 11/7/2026full-timeLockbourne • Ohio • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and deploying machine learning models, with strong proficiency in Python and SQL. Capable of leading complex analytical initiatives and collaborating across teams to drive data-driven decision-making and AI strategy.

Highest-signal resume keywords

Machine Learning Model DevelopmentStatistical ModelingCloud Platforms (AWS, Azure, GCP)Data Processing Technologies (Spark, Databricks)Strong Communication Skills

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonSQLMachine LearningStatistical ModelingFeature EngineeringHyperparameter TuningModel EvaluationDeep Learning Frameworks (PyTorch, TensorFlow)NLPMLOps

Soft Skills

Stakeholder ManagementMentoringCollaborationCommunication

Tools & Technologies

Cloud TechnologiesDistributed Computing FrameworksAPIsDecision-Support ToolsDashboards

Industry Keywords

Data ScienceApplied AIAdvanced AnalyticsSupply ChainHealthcareFinancial ServicesCustomer Analytics

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformPythonPyTorchSparkSQLTensorflow

About the role

Key responsibilities & impact

Develop, validate, deploy, and monitor machine learning models for forecasting, optimization, customer intelligence, operational decision-making, and business growth
Design and implement supervised and unsupervised predictive models
Perform feature engineering, model selection, hyperparameter tuning, and performance evaluation
Establish model governance processes, including documentation, explainability, fairness assessments, and monitoring
Translate business challenges into analytical frameworks and data-driven solutions
Conduct exploratory analysis, statistical testing, forecasting, and causal analysis
Present findings and recommendations to technical and non-technical audiences, including senior leadership
Create dashboards, analytical products, and decision-support tools
Partner with data engineers on scalable data pipelines and feature stores
Work with large structured and unstructured datasets across cloud environments
Build and optimize data workflows using cloud technologies and distributed computing frameworks
Support data quality, governance, and reproducibility standards
Collaborate with Product, Operations, Finance, Supply Chain, Sales, and IT teams
Mentor junior data scientists and analysts
Contribute to the organization’s AI strategy, best practices, and technical standards roadmap
Lead analytical projects from ideation through deployment and business adoption
Within the first year, deliver measurable machine learning value, build stakeholder relationships, improve analytical decision-making, contribute to AI strategy, and help build scalable data science capabilities

Requirements

What you’ll need

Bachelor’s degree in Data Science, Statistics, Computer Science, Engineering, Mathematics, Economics, Operations Research, or a related quantitative field
5+ years of professional experience in Data Science, Machine Learning, Applied AI, or Advanced Analytics
Strong proficiency in Python and SQL
Experience developing and deploying machine learning models in production environments
Deep understanding of statistical modeling, machine learning algorithms, and model evaluation techniques
Experience with cloud platforms such as AWS, Azure, or GCP
Experience with large-scale data processing technologies such as Spark, Databricks, or distributed computing frameworks
Strong communication and stakeholder management skills
Demonstrated ability to independently lead complex analytical initiatives
Preferred: Ph.D. in a quantitative discipline
Preferred: Experience with deep learning frameworks such as PyTorch or TensorFlow
Preferred: Experience with NLP, LLMs, generative AI, and agent-based AI systems
Preferred: Experience deploying APIs, machine learning services, or AI applications
Preferred: Experience with supply chain, manufacturing, healthcare, financial services, or customer analytics
Preferred: Experience with MLOps, CI/CD, model monitoring, and model governance frameworks
Preferred: Published research, patents, conference presentations, or significant contributions to open-source projects