Senior Machine Learning Engineer

GTT•Philadelphia, PA

19d•Onsite

About The Position

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.

Requirements

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.
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.

Nice To Haves

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.

Responsibilities

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.
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.
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 into .NET/TypeScript-based products on Azure is a plus.
Ensure models are observable, supportable, secure, and aligned with architecture and governance expectations.
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.
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.
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.