Senior Development Operations Engineer (Data Science) at Belcan
Date: 4 hours ago
City: Normal, IL
Contract type: Full time
Job Description
Job Title: Senior Development Operations Engineer (Data Science)
Location: Normal, IL
Zip Code: 61761
Duration:12 Months
Pay Rate: $ 72.59/hr
Keyword's: #Normaljobs; #OperationsEngineerjobs.
Start Date: Immediate
Job Description:
Key Responsibilities
Qualifications
Must Have:
Job Title: Senior Development Operations Engineer (Data Science)
Location: Normal, IL
Zip Code: 61761
Duration:12 Months
Pay Rate: $ 72.59/hr
Keyword's: #Normaljobs; #OperationsEngineerjobs.
Start Date: Immediate
Job Description:
Key Responsibilities
- ML Technical Leadership - Define ML architecture, best practices, and performance standards for enterprise-scale solutions.
- End-to-End Model Development - Lead the full lifecycle from data preprocessing and feature engineering to training, validation, deployment, and monitoring.
- Traditional ML Expertise - Apply algorithms such as regression, tree-based models, SVMs, clustering, and forecasting to solve high-impact problems ,feature engineering and hyper parameter tuning.
- Programming & Integration - Build scalable ML pipelines and APIs in Python (primary) and Golang (for backend services).
- MLOps Implementation - Design and manage CI/CD pipelines for ML, including automated retraining, model versioning, monitoring, and rollback strategies.
- Statistical Analysis - Apply hypothesis testing, Bayesian methods, and model interpretability techniques to ensure reliable insights.
- Cross-Functional Collaboration - Partner with engineering, analytics, and product teams to align technical solutions with business objectives.
Qualifications
Must Have:
- 8+ years of experience in applied ML or data science, including 3+ years in a senior or staff-level role and devops experience.
- Expert proficiency in Python for ML development (Good to have: Golang for backend integration)
- Proven experience deploying traditional ML models to production with measurable business impact.
- Strong knowledge of ML frameworks (Scikit-learn, XGBoost, LightGBM) and data libraries (Pandas, NumPy, Statsmodels).
- Hands-on MLOps experience with tools like MLflow (preferred), Databricks(preferred), Kubeflow, Vertex AI Pipelines, or AWS SageMaker Pipelines.
- Experience with model monitoring, drift detection, and automated retraining strategies.
- Strong database skills (SQL and NoSQL).
- Masters degree or PHD is mandatory
- Exposure to retrieval-augmented generation (RAG) pipelines and vector databases.
- Time-series analysis and anomaly detection experience.
- Cloud deployment expertise (AWS, Azure, GCP).
- Familiarity with distributed computing frameworks (Spark, Ray).
- Strategic problem-solver with the ability to align AI solutions to business goals.
- Excellent communicator across technical and non-technical stakeholders.
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