Remote MLE - INTL Nearshore

Insight Global • Colorado Springs, CO • 17h ago

Job Description

Insight Global is looking for a Machine Learning Engineer/Data Scientist to help lead the design, development, and deployment of scalable machine learning models that power business decisions across this company's enterprise. This role combines technical depth in ML/AI with a strong understanding of business domains such as Sales, Service, Finance, Order Fulfillment, and Supply Chain. You will collaborate closely with Data Scientists, Data Engineers, and business partners to build production-ready solutions that drive measurable impact.

Responsibilities:

Machine Learning Development & Deployment

-Design and implement supervised and unsupervised models for predictive analytics, including churn prediction, demand forecasting, renewal risk scoring, and cross-sell/upsell opportunity identification.

-Translate business problems into ML frameworks and production solutions that improve efficiency, revenue, or customer experience.

-Build, optimize, and maintain ML pipelines using tools such as MLflow, Airflow, or Kubeflow.

Cross-Functional ML Use Cases

-Partner with teams across Sales (e.g., lead scoring, next-best action), Customer Service (e.g., case deflection, sentiment analysis), Finance (e.g., revenue forecasting, fraud detection), Supply Chain (e.g., inventory optimization, ETA prediction), and Order Fulfillment (e.g., delivery risk modeling) to define impactful ML use cases.

-Develop domain-specific models and continuously improve them using feedback loops and real-world performance data.

Model Governance and MLOps

-Ensure robust model monitoring, versioning, and retraining strategies to keep models reliable in dynamic environments.

-Work closely with DevOps and Data Engineering teams to automate deployment, CI/CD workflows, and cloud-native ML infrastructure (AWS/GCP/Azure).

Data Engineering and Feature Architecture

-Collaborate with data engineers to define feature stores, data quality checks, and model-ready datasets on platforms like Snowflake or Databricks.

-Perform feature selection, transformation, and engineering aligned with each domain's business logic.

Communication & Stakeholder Collaboration

-Present technical insights and model results to business and executive stakeholders in a clear, actionable format.

-Work with Product Owners and Program Managers to scope, prioritize, and plan delivery of ML projects.

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/.

Skills and Requirements

-8+ years of experience in machine learning, data science, or AI engineering, with a strong software engineering background

-Experience in Python creating ML models from scratch

-Experience with libraries such as scikit-learn, XGBoost, PyTorch, TensorFlow, or similar

-Experience deploying models into production using ML pipelines and orchestration frameworks

-Strong understanding of data structures (preferred experience with enterprise data platforms i.e. Snowflake, Oracle Fusion, Salesforce)

-Advanced SQL experience and ability to do data profiling, table creation, and validation

-Experience deploying models on cloud platforms (AWS SageMaker, Azure ML, GCP Vertex AI, or Databricks ML)

-Experience with CI/CD pipelines +Experience at a large consumer/ecommerce company (META, Apple, Netflix, Google, Wayfair, etc.)

+Exposure to enterprise data platforms (e.g., Snowflake, Oracle Fusion, Salesforce).

+Docker or Kubernetes experience (or any other container tools)

+LLM experience

+Familiarity with MLOps tools (MLflow, SageMaker Pipelines, Feature Store)

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