Data Scientist

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.