Data Scientist with Machine Learning Engineer
Summary
Build and deploy ML models and pipelines using Python, TensorFlow/PyTorch, and MLOps tools; analyze data to drive business decisions.
Data Scientist with Machine Learning Engineer
1 week ago Be among the first 25 applicants
Key Responsibilities
- Explore, clean, and analyze structured and unstructured datasets.
- Apply statistical techniques to generate insights and support business decisions.
- Develop predictive models using regression, classification, clustering, NLP, or time‑series methods.
- Conduct hypothesis testing, A/B experiments, and exploratory data analysis (EDA).
Machine Learning Engineering
- Build, train, and fine‑tune ML models using frameworks such as TensorFlow, PyTorch, Scikit‑learn.
- Develop and maintain end‑to‑end ML pipelines, including feature engineering, model training, evaluation, and versioning.
- Deploy ML models into production using MLOps pipelines and tools like MLflow, Kubeflow, SageMaker, or Vertex AI.
- Implement monitoring, retraining strategies, and improve model performance.
- Optimize models for scalability, latency, and resource efficiency.
Must-Have
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, AI/ML, or related field.
- 3–7+ years of experience as a Data Scientist, ML Engineer, or similar role.
- Strong programming skills in Python (NumPy, Pandas, Scikit‑learn, Matplotlib, etc.).
- Experience with ML frameworks (TensorFlow, PyTorch).
- Strong understanding of machine learning algorithms, statistics, and data modeling.
- Experience deploying models in production (Docker, APIs, cloud deployment).
- Knowledge of SQL and experience with relational and NoSQL databases.
- Familiarity with MLOps tools (MLflow, Kubeflow, Airflow, DVC).
Seniority level
Entry level
Employment type
Full-time
Job function
Engineering and Information Technology
Industries
IT Services and IT Consulting