Senior Machine Learning Engineer

Location: Philadelphia PA

Onsite Flexibility: Onsite

• Position Type: Right to Hire (Contract-to-Hire)
• Pay / Salary: $160000 / Year (USD)
• Work Authorization: Applicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.

We are looking for a Senior Machine Learning Engineer to work hands-on on machine learning predictive modeling scoring decisioning and applied AI initiatives. This role is focused on building validating deploying and improving machine learning models as a strong individual contributor working alongside senior technical leadership who will help shape problem definition and overall model strategy.

This is a hands-on model-building role. The ideal candidate should be comfortable spending most of their time working directly with data features models scoring logic validation methods production workflows and model improvement and should bring solid engineering judgment ownership of their deliverables and the ability to drive their work forward without waiting for perfect requirements.

We are especially interested in candidates with experience building predictive scores risk scores health scores engagement scores prioritization models or similar decision-support systems. Experience with transparent interpretable and explainable models is valuable especially in environments where business trust auditability and operational adoption matter.

This is a fast-moving startup-like environment. Requirements may be incomplete and priorities may evolve; the right candidate is comfortable iterating quickly and helping create clarity within their own workstream. A background in commercial software SaaS digital products fintech healthtech consumer technology or other product-driven environments is preferred.

Experience with Generative AI is also useful especially where LLMs RAG summarization conversational AI agents document intelligence or AI-enabled workflow automation can complement traditional predictive models and scoring systems.

The Top Three Things We Are Looking For

1. Strong Hands-On Production ML Builder The right candidate must be able to personally build models. They should be comfortable taking messy data and a defined prediction problem and turning it into a working validated usable model including feature engineering model training validation calibration thresholding monitoring production scoring and model improvement.

2. Product and Commercial Software Mindset The right candidate should think beyond model performance and understand how models become useful product capabilities: who will use the model what decision it supports what action it should trigger and how success will be measured. Experience in commercial software SaaS fintech healthtech consumer products fraud credit pricing personalization or similar product-driven environments is valuable.

3. Experience with Transparent Scoring and Decisioning Because this role supports scores and decisioning systems we value candidates who have built models that people can understand trust monitor and act on scorecards risk scores health scores calibrated models score bands thresholds and business-facing model explanations. Strong candidates without direct scorecard experience but with solid interpretable-modeling fundamentals will also be considered.

Hands-On Model Development

• Build test validate and improve machine learning models for scoring prediction prioritization risk detection engagement and decision support.
• Perform exploratory data analysis data quality assessment feature engineering model training model selection and performance evaluation.
• Develop practical 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.

Scoring and Transparent Models

• Implement predictive scores risk tiers score bands thresholds cut points and intervention logic based on agreed designs.
• Build transparent and interpretable models where explainability matters including logistic regression GLMs decision trees calibrated models or explainable boosting approaches.
• Evaluate models for accuracy calibration stability drift and operational usefulness.
• Document model logic features assumptions limitations and validation results in a way that business and technical stakeholders can understand.

Production ML and MLOps

• Partner with data engineering platform engineering and application engineering teams to move models from experimentation into reliable production workflows.
• Support model deployment batch scoring real-time or near-real-time inference model versioning monitoring retraining and performance tracking.
• Expose models as well-documented services/APIs consumable by application teams; familiarity integrating ML capabilities /TypeScript-based products on Azure is a plus.
• Ensure models are observable supportable secure and aligned with architecture and governance expectations.

Product and Rapid-Build Execution

• Operate effectively in a rapid-build startup-like environment where speed ownership and pragmatic decision-making matter.
• Turn defined business needs and rough concepts into working ML prototypes and production capabilities iterating based on feedback.
• Make smart tradeoffs between quick prototypes transparent models GenAI-enabled workflows and longer-term maintainability with guidance from technical leadership.

Generative AI and AI Automation

• Contribute to GenAI-enabled solutions including LLM-powered workflows RAG summarization conversational agents and document intelligence.
• Help evaluate when GenAI is appropriate versus traditional ML rules analytics or transparent scoring models.
• Apply appropriate evaluation guardrails monitoring privacy controls and human-in-the-loop processes for GenAI use cases.

Stakeholder Collaboration

• Work with business product analytics and engineering stakeholders to clarify what a model is intended to predict explain recommend or trigger.
• Translate business questions into measurable ML objectives target variables features validation approaches and success metrics with support from senior technical leadership.
• Communicate model behavior tradeoffs limitations and recommended usage clearly to both technical and non-technical audiences.
• Participate in code reviews and design reviews and contribute to team standards for model development validation documentation and production readiness.

• 5 years of professional experience in machine learning data science software engineering analytics engineering applied AI or related technical fields.
• 3 years of hands-on machine learning model development experience including feature engineering model training validation evaluation and iteration.
• 2 years of experience deploying operationalizing or supporting models in production or business-critical environments.

• 7 years of relevant professional experience in ML data science applied AI or production analytics.
• Experience building scorecards risk scores health scores engagement scores churn scores fraud scores or operational decision-support models.
• Experience with transparent or interpretable models such as logistic regression GLMs GAMs decision trees calibrated models or Explainable Boosting Machines.
• Experience in commercial software SaaS digital products fintech healthtech consumer technology or other product-driven environments.
• Experience in startup scale-up or rapid-build environments requiring independent execution amid ambiguity.
• Experience with GenAI LLMs RAG AI agents prompt engineering model evaluation or AI-enabled workflow automation.
• Experience in healthcare population health remote patient monitoring insurance financial services or other domains where model trust and explainability are important.
• Experience with MLOps practices including model registries deployment pipelines monitoring drift detection and retraining strategies.
• Experience delivering ML within an Azure-centric application environment (.NET / TypeScript services) or supporting teams through a platform modernization.

• Strong hands-on experience with Python and SQL.
• Experience with modern ML and data platforms with Azure strongly preferred (Azure ML Azure Databricks Spark MLflow Snowflake or similar).
• Solid understanding of model evaluation calibration thresholding monitoring drift retraining and the production ML lifecycle.
• Ability to explain model behavior 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 hands-on senior contributor within a defined workstream.

• Ideal candidate traits include:

- Hands-on: comfortable spending most of their time building testing validating and improving models. - Practical: knows when a simple transparent model is better than a complex one. - Product-minded: understands that models need to improve decisions workflows outcomes or business results. - High ownership: identifies what needs to be done within their workstream and drives it forward. - Startup comfortable: can operate with evolving priorities imperfect information and rapid iteration. - Evidence-driven: wants to prove that a model is valid stable explainable actionable and useful. - Technically rigorous: cares about data quality calibration monitoring reproducibility and production readiness. - Collaborative: works well with business product analytics engineering and operations stakeholders. - Clear communicator: can explain model design and tradeoffs without hiding behind jargon.

• Success in this role looks like:

- High-quality models and scores are built validated deployed monitored and improved over time. - Model outputs are explainable and trusted by business and operational stakeholders. - Scores are connected to real decisions workflows interventions or measurable outcomes. - Models ship quickly iterate based on feedback and mature from prototype to production without over-engineering. - Work is documented reproducible and production-ready meeting team standards for model development and monitoring.

• Medical Vision and Dental Insurance Plans
• 401k Retirement Fund

GTT is a minority-owned staffing firm and a subsidiary of Chenega Corporation a Native American-owned company in Alaska. We highly value diverse and inclusive workplaces and support Fortune 500 organizations across banking financial services technology life sciences biotech utilities and retail sectors throughout the U.S. and Canada.

Job Number: 26-11393 Industry: Manufacturing & Operations

#LI-Hybrid #LI-GTT