Job Specifications
Job Title: Data Scientist - Machine Learning
Location: Remote
Employment Type: Full-time
Experience Level: 7+ years
Key Responsibilities
Develop, train, and deploy machine learning and statistical models to solve complex business problems.
Analyze large, structured and unstructured datasets to extract actionable insights.
Build and optimize end-to-end ML pipelines -- from data preprocessing to model deployment.
Work with cross-functional teams to define measurable success metrics for ML solutions.
Use Python, TensorFlow, PyTorch, Scikit-learn, and AWS/GCP/Azure ML tools for experimentation and production.
Design and implement feature engineering, model evaluation, and performance tuning strategies.
Collaborate with data engineering teams to ensure robust data availability and pipeline scalability.
Communicate analytical findings and recommendations to technical and non-technical stakeholders.
Stay current with emerging ML techniques, tools, and best practices.
Required Qualifications
7+ years of experience in Data Science, with a strong focus on machine learning and applied AI.
Proficiency in Python, and solid experience with ML libraries such as Scikit-learn, TensorFlow, PyTorch, XGBoost, etc.
Strong background in statistics, probability, and data modeling.
Experience deploying ML models into production using MLOps frameworks (e.g., SageMaker, MLflow, Kubeflow).
Hands-on experience with SQL and working with large datasets in distributed systems (Spark, Hadoop).
Deep understanding of supervised and unsupervised learning, feature selection, and model interpretability.
Excellent communication skills and ability to explain complex concepts to non-technical audiences.
Preferred Qualifications
Master's or Ph.D. in Computer Science, Statistics, Mathematics, or related field.
Experience with deep learning, NLP, time series forecasting, or recommendation systems.
Familiarity with cloud-based ML environments (AWS SageMaker, Google Vertex AI, or Azure ML).
Knowledge of data versioning, CI/CD for ML, and containerization (Docker, Kubernetes).
Strong business acumen and experience translating analytical outcomes into strategic decisions.