Machine Learning Engineer / Data Scientist (Mid-Level)

Key Responsibilities: • Model Development: Design, train, and evaluate machine learning models to solve business problems such as classification, regression, clustering, and recommendation. • Data Preparation: Work with large and complex datasets, performing data cleaning, preprocessing, and feature engineering to optimize model performance. • Model Deployment: Deploy machine learning models into production environments, ensuring scalability and robustness. • Algorithm Selection: Research and implement state-of-the-art algorithms and methodologies to enhance model accuracy and efficiency. • Collaboration: Collaborate with data engineering teams to build data pipelines and ensure efficient data flow for machine learning workflows. • Visualization and Reporting: Create clear and actionable visualizations and reports to communicate findings and results to stakeholders. • Monitoring and Maintenance: Monitor model performance in production, address drift or bias issues, and optimize models as needed. • Tool Development: Build tools and frameworks to enable rapid experimentation and iteration of machine learning models. • Documentation: Maintain comprehensive documentation for models, experiments, and processes. Qualifications: Education: Bachelor s or Master s degree in Computer Science, Data Science, Machine Learning, Statistics, or a related field (or equivalent experience). Technical Skills: • Strong programming skills in Python, R, or similar languages. • Experience with machine learning libraries and frameworks such as TensorFlow, PyTorch, Scikit-learn, or Keras. • Proficiency in data manipulation and analysis using tools like Pandas, NumPy, and SQL. • Experience with big data technologies such as Spark, Hadoop, or similar. • Knowledge of cloud platforms and services (e.g., AWS SageMaker, Google AI Platform, Azure ML). • Familiarity with MLOps practices and tools for CI/CD in machine learning workflows. • Understanding of data visualization tools like Matplotlib, Seaborn, or Tableau. • Strong grasp of statistical methods, probability, and optimization techniques. Experience: • 3 5 years of experience in machine learning, data science, or a related field. • Proven experience building and deploying machine learning models in production. • Experience with natural language processing (NLP), computer vision, or time-series analysis is a plus. Soft Skills: • Strong problem-solving and analytical thinking abilities. • Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders. • Ability to work independently and collaboratively within a team. • Curiosity and eagerness to stay updated on the latest advancements in machine learning and AI. Education: Bachelor s or Master s degree in Computer Science, Data Science, Machine Learning, Statistics, or a related field (or equivalent experience). Technical Skills: • Strong programming skills in Python, R, or similar languages. • Experience with machine learning libraries and frameworks such as TensorFlow, PyTorch, Scikit-learn, or Keras. • Proficiency in data manipulation and analysis using tools like Pandas, NumPy, and SQL. • Experience with big data technologies such as Spark, Hadoop, or similar. • Knowledge of cloud platforms and services (e.g., AWS SageMaker, Google AI Platform, Azure ML). • Familiarity with MLOps practices and tools for CI/CD in machine learning workflows. • Understanding of data visualization tools like Matplotlib, Seaborn, or Tableau. • Strong grasp of statistical methods, probability, and optimization techniques. Experience: • 3 5 years of experience in machine learning, data science, or a related field. • Proven experience building and deploying machine learning models in production. • Experience with natural language processing (NLP), computer vision, or time-series analysis is a plus. Soft Skills: • Strong problem-solving and analytical thinking abilities. • Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders. • Ability to work independently and collaboratively within a team. • Curiosity and eagerness to stay updated on the latest advancements in machine learning and AI.