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Data Science Lead (MLOps & Advanced Analytics)

1 day, 16 hours ago
Mexico, Argentina, Bolivia, Colombia, Costa Rica, Ecuador, Paraguay, Peru, Uruguay
Contract
Lead
Data Scientist
Data Science and Analytics
Agile Apache Spark Azure Azure Pipelines CI/CD Databricks Docker Kubeflow Kubernetes LightGBM LSTM Machine Learning MLflow MLOps Neural Networks Pandas Python Scikit-learn Scrum SQL TensorFlow XGBoost
Apply Now
Resilient Co

Resilient Co

Resilient Co is a technology consulting company that empowers businesses with smart solutions and diverse teams, offering resilient support in the dynamic tech industry.

Professional Services
11-50
Founded 2020
View All Jobs 12

Description

  • Lead the design and delivery of end-to-end machine learning systems with MLOps principles.
  • Oversee the full model lifecycle from hypothesis and EDA through feature engineering, model selection, and production deployment.
  • Serve as the technical bridge between data engineering teams and business stakeholders.
  • Architect automated pipelines for data validation, model profiling, and hyperparameter tuning in Azure Machine Learning.
  • Establish monitoring for data drift and concept drift after deployment.
  • Ensure models are reproducible, auditable, scalable, and production-ready.
  • Translate business KPIs into objective functions for ML development.

Requirements

  • 5–8 years of total experience, including 4+ years in hands-on data science and 2+ years leading technical teams or complex projects.
  • BE/BS or MS/PhD in Computer Science, Statistics, Mathematics, Physics, or a related quantitative field.
  • Strong proficiency in Python, including Pandas, Scikit-learn, PySpark, and/or TensorFlow, plus SQL.
  • Experience with supervised and unsupervised learning, including XGBoost, LightGBM, Random Forests, and neural networks.
  • Mastery of hypothesis testing, Bayesian inference, and error analysis, with experience designing experiments and A/B tests.
  • Experience with advanced forecasting methods such as Prophet, ARIMA, or LSTM networks, especially for supply chain or revenue use cases (highly desirable).
  • Knowledge of loss function customization and optimization algorithms such as Gradient Descent and Genetic Algorithms.
  • Hands-on experience with PySpark and Databricks for distributed processing of large-scale datasets.
  • Expert-level knowledge of MLflow for experiment tracking and Kubeflow or Azure Pipelines for orchestration.
  • Experience with Docker/Kubernetes and deploying models as batch inference jobs.
  • Ability to translate ambiguous business problems into specific ML problem statements (nice to have).
  • Experience conducting code reviews, promoting clean code, and mentoring junior data scientists (nice to have).
  • Familiarity with Agile/Scrum methodologies and adapting sprint cycles for R&D work (nice to have).
  • Ability to explain model trade-offs to non-technical executives and influence stakeholders (nice to have).

Benefits

  • 3-month initial contract with rolling quarterly renewals based on performance.
  • Standard EST working hours (8:00 AM to 5:00 PM).
  • No overtime required.
  • Bring-your-own-device (BYOD) arrangement.

Interested in this position?

Apply directly on the company website

Apply Now

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