Sr Machine Learning Engineer

The Senior Machine Learning Engineer will design, build, and operationalize machine learning solutions that power major initiatives for one of our largest clients. This role sits at the intersection of data engineering, data science, and software engineering, with a focus on scalable ML systems that drive measurable business impact across offers, campaigns, and customer experiences.

You will partner closely with Product, Marketing, and Digital teams to translate business problems into ML solutions, leveraging large scale customer, transaction, clickstream, and third party data assets.
Day to Day duties:
- Partner with data engineering to shape and consume domain assets across customer, transactions, store, redemptions, communications, clickstream, and 3P Acxiom/syndicated data.
- Design and implement robust feature pipelines (batch and streaming) that feed a shared feature store, with emphasis on behavioral, time series, and journey level features.
- Build, evaluate, and productionize models using appropriate techniques (e.g., tree based methods, GLMs, uplift models, recommender systems, bandits; deep learning where appropriate).
- Implement model serving patterns for both batch and real time use cases using APIs and/or streaming frameworks.
- Own monitoring, drift detection, retraining strategies, and performance reporting for models in production.
- Design and analyze A/B tests and other experimental frameworks to measure causal lift and incrementality of ML driven interventions.
- Partner with analytics and product teams to interpret results and iterate on models and strategies.
- Ensure models adhere to standards for data quality, privacy, and responsible AI.
- Provide clear, human readable explanations of model behavior, limitations, and risk trade offs for non technical stakeholders.
- Contribute to documentation, model cards, and reusable templates that support auditability and governance.
- Work in cross functional Agile teams with data engineers, data scientists, product managers, and marketers.
- Mentor junior engineers and analysts on ML engineering best practices, code quality, and production mindset.
- Help shape the technical roadmap for front store ML, including adoption of feature stores, real time decisioning, and future agentic/LLM driven capabilities.

Compensation:
$58-62/hour

Exact compensation may vary based on several factors, including skills, experience, and education.

Benefit packages for this role will start on the 1st day of employment and include medical, dental, and vision insurance, as well as HSA, FSA, and DCFSA account options, and 401k retirement account access with employer matching. Employees in this role are also entitled to paid sick leave and/or other paid time off as provided by applicable law.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.

- 5+ years of hands on experience delivering machine learning solutions into production environments.
- Strong programming skills in **Python** and **SQL**, with experience in common ML libraries (e.g., scikit learn, PySpark ML, XGBoost/lightGBM).
- Proven experience building data pipelines and features from large scale, complex datasets (e.g., customer, transaction, or clickstream data) using tools such as Spark, Airflow, or similar.
- Experience deploying models as services or workflows (e.g., APIs, batch jobs, streaming inference) and integrating with production systems.
- Demonstrated ability to design experiments (A/B tests) and evaluate uplift / incrementality.
- Solid understanding of software engineering fundamentals: version control (Git), testing, CI/CD concepts, code review practices.
- Strong communication skills with the ability to explain technical concepts and trade offs to non technical partners.

- Bachelor's degree in computer science, Data Science, Statistics, Engineering, or related field required OR Master’s or PhD in a quantitative field preferred (or equivalent industry experience).

- Experience in retail, e commerce, ad tech, or other high volume personalization environments.
- Familiarity with feature stores, real time streaming technologies (e.g., Kafka/Kinesis), and low latency model serving.
- Exposure to recommender systems, bandit algorithms, or reinforcement style personalization.
- Experience working with or around semantic layers, identity graphs, or customer 360 platforms.
- Awareness of LLM/agentic AI patterns and how they interact with structured data and ML systems.
- Prior experience mentoring peers and influencing technical direction across teams.

Benefit packages for this role will start on the 1st day of employment and include medical, dental, and vision insurance, as well as HSA, FSA, and DCFSA account options, and 401k retirement account access with employer matching. Employees in this role are also entitled to paid sick leave and/or other paid time off as provided by applicable law.