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Head Of Machine Learning Jobs in Springfield, PA

Vanguard

Senior Machine Learning Engineer

Malvern, PA · On-site

$102K - $140K/yr

Minimum of eight years related work experience, with at least three years of development experience ... Experience in software engineering, machine learning engineering, data engineering, or a related ...

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Head Of Machine Learning information

See Springfield, PA salary details

$23.1K

$60.2K

$102.8K

How much do head of machine learning jobs pay per year?

As of Sep 11, 2026, the average yearly pay for head of machine learning in Springfield, PA is $60,244.00, according to ZipRecruiter salary data. Most workers in this role earn between $47,200.00 and $70,800.00 per year, depending on experience, location, and employer.

What does a head of machine learning do?

A Head of Machine Learning leads the development and implementation of machine learning strategies within an organization. They oversee data science teams, manage AI-driven projects, and ensure models are scalable and aligned with business needs. This role requires expertise in machine learning, software engineering, and leadership to drive innovation and improve decision-making through data.

What are the key skills and qualifications needed to thrive as a head of machine learning?

To thrive as a Head Of Machine Learning, you need deep expertise in machine learning algorithms, statistical modeling, and data analysis, usually supported by an advanced degree in computer science or a related field. Familiarity with Python, TensorFlow, PyTorch, cloud computing platforms, and relevant certifications (like AWS Certified Machine Learning) is highly beneficial. Strong leadership, strategic thinking, and communication skills set exceptional candidates apart by enabling effective team management and cross-departmental collaboration. These skills are crucial to drive innovation, deliver impactful projects, and steer organizational AI strategies successfully.

What are some typical challenges faced by a head of machine learning, and how can I prepare for them?

As a Head Of Machine Learning, you’ll often face challenges such as aligning machine learning initiatives with business objectives, managing a diverse technical team, and ensuring the scalability and reliability of solutions. Preparing for these involves staying updated on the latest AI trends, developing strong project management skills, and fostering a culture of knowledge sharing within your team. Additionally, you may need to bridge communication gaps between technical staff and non-technical stakeholders, so clear communication is vital. By proactively addressing these areas, you’ll be better equipped to lead successful machine learning operations and drive significant business value.

Is a Head of Machine Learning a high paying job?

A Head of Machine Learning is typically a high-paying role due to its seniority and specialized expertise in AI, data science, and leadership. Salaries often reflect experience, industry, and company size, with many earning well above average tech salaries, especially in competitive markets.

What cities near Springfield, PA are hiring for Head Of Machine Learning jobs?

Cities near Springfield, PA with the most Head Of Machine Learning job openings:

Head Of Machine Learning jobs near you
Infographic showing various Head Of Machine Learning job openings in Springfield, PA as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 22% Part Time, and 2% Contract. Highlights an 85% Physical, 2% Hybrid, and 13% Remote job distribution, with an average salary of $60,244 per year, or $29 per hour.

Principal Machine Learning Engineer

Philadelphia, PA • On-site

Apetan Consulting llc
IT Services • 1 - 10 employees
Learn more about Apetan Consulting llcexternal

$80 - $150/hr

Contractor

Re-posted 26 days ago

Job description

Title: Principal Machine Learning Engineer

Duration: 6 Mos C2H (without sponsorship)

Location: Hybrid in Philadelphia, PA onsite Tue & Wed each week (Local candidates preferred but, those willing to relocate are acceptable)

Principal Machine Learning Engineer to serve as a hands-on technical leader for machine learning, predictive modeling, scoring, decisioning, and applied AI initiatives. This role will primarily focus on building, validating, deploying, and improving machine learning models, while also bringing principal-level judgment to problem definition, model design, stakeholder engagement, and production readiness.

Hands-On Model Development

  • Build, test, validate, and improve machine learning models for scoring, prediction, prioritization, risk detection, engagement, intervention targeting, and decision support.
  • Perform exploratory data analysis, data quality assessment, feature engineering, model training, model selection, and performance evaluation.
  • Develop practical ML models that balance predictive performance, explainability, stability, maintainability, and business usefulness.
  • Work with structured, semi-structured, and operational data to create model-ready datasets and reusable features.
  • Use tools such as Python, SQL, Spark, Databricks, MLflow, scikit-learn, XGBoost, or similar platforms and libraries.
  • Move quickly from data exploration to prototype to validated model to production-ready capability.

 

Required Qualifications

  • Professional experience in machine learning, data science, software engineering, analytics engineering, applied AI, or related technical fields.
  • 5+ years of hands-on machine learning model development experience, including feature engineering, model training, validation, evaluation, and iteration.
  • 3+ years of experience deploying, operationalizing, or supporting models in production or business-critical environments.
  • Strong hands-on experience with Python and SQL.
  • Experience with modern ML and data platforms such as Databricks, Spark, MLflow, Snowflake, Azure, AWS, or similar technologies.
  • Strong understanding of model evaluation, calibration, thresholding, score interpretation, monitoring, drift, retraining, and production ML lifecycle management.
  • Experience translating ambiguous business problems into concrete ML designs, model requirements, validation plans, and measurable outcomes.
  • Ability to explain model behavior, model performance, assumptions, limitations, and tradeoffs to both technical and non-technical stakeholders.
  • Strong engineering discipline, including clean code, reproducibility, versioning, testing, documentation, and maintainability.
  • Ability to work independently as a senior hands-on contributor while also providing technical leadership and modeling judgment.

Scoring, Scorecards, and Transparent Models

Production ML and MLOps

Product and Rapid-Build Execution

Generative AI and AI Automation

Requirement Shaping and Stakeholder Partnership