Data Scientist

Hello,Please find the JD below and let me know your interest with an updated resume.Data ScientistGuadalajara, MX Remote and Onsite all 5 days – Mexico6-12 Months ContractPosition requirements Guadalajara, MX – 5 days in the Office - To start, may be remote, but in 3-6 weeks will be OnsiteJob DescriptionResponsibilitiesDesign and build end-to-end data pipelines : ingestion, transformation, labeling, feature engineering, training, validation, and deployment.Develop and maintain reproducible ML research workflows using Jupyter / Colab notebooks and version-controlled environments.Lead data labeling strategy : define annotation guidelines, ensure data quality, and automate labeling where possible.Conduct exploratory data analysis (EDA) and create scalable datasets for supervised and unsupervised ML.Train, evaluate, and optimize ML models, ensuring performance across multiple environments (dev, staging, prod).

Implement MLOps best practices : experiment tracking, pipeline automation, CI / CD for ML, and monitoring model drift.Collaborate with cross-functional teams (ML engineers, software developers, product managers) to translate research outcomes into production-ready solutions.PhD in Computer Science, Data Science, Statistics, Applied Mathematics, or related field.3+ years of research and applied industry experience in data science or ML.Proven experience in designing, building, and maintaining end-to-end ML pipelines.Strong programming skills in Python, with expertise in libraries like pandas, NumPy, scikit-learn, TensorFlow / PyTorch.Proficiency with notebook-based experimentation and data labeling pipelines.Solid understanding of statistical modeling, machine learning algorithms, and data quality management.Experience with SQL and data warehouses / lakes for large-scale data processing.Hands-on experience with MLOps tools (MLflow, Kubeflow, Airflow, Vertex AI, SageMaker).

Familiarity with cloud platforms (GCP, and containerization (Docker / Kubernetes).

Experience automating data labeling workflows (e.g., active learning, weak supervision).