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

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. Desired Candidate Profile

Education

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

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