Machine Learning Engineer (Traditional ML)

We are seeking a Machine Learning Engineer with strong expertise in traditional ML algorithms and statistical modeling techniques. The role involves developing, training, testing, and deploying ML models to solve business problems, improve decision-making, and optimize processes. The ideal candidate should have a solid background in mathematics, statistics, and programming, with hands-on experience in applying classical ML techniques rather than relying solely on deep learning frameworks.

Key Responsibilities:

• Design, develop, and implement machine learning models using traditional algorithms (e.g., regression, SVM, decision trees, ensemble methods, clustering, time series forecasting).
  
• Perform data preprocessing, feature engineering, and exploratory data analysis (EDA) to extract meaningful insights.
  
• Build scalable and efficient ML pipelines for model training and evaluation.
  
• Conduct hypothesis testing and statistical analysis to validate models and assumptions.
  
• Collaborate with data engineers, analysts, and business teams to identify ML opportunities and translate requirements into technical solutions.
  
• Optimize models for performance, accuracy, and scalability.
  
• Monitor and maintain deployed models, ensuring reliability and continuous improvement.
  
• Document methodologies, processes, and results for transparency and reproducibility.
  

Required Skills & Qualifications:

• Bachelors or Master’s degree in Computer Science, Statistics, Mathematics, Data Science, or a related field.
  
• Strong programming skills in

  Python, R, or Java

  with experience in ML libraries (e.g., scikit-learn, XGBoost, LightGBM, statsmodels).
• Proficiency in statistical methods, probability theory, and hypothesis testing.
  
• Solid understanding of supervised and unsupervised learning techniques.
  
• Experience with

  feature engineering, dimensionality reduction, and model evaluation metrics

  .
• Familiarity with

  SQL

  and data wrangling tools.
• Strong problem-solving and analytical skills with attention to detail.
  
• Ability to work with large, complex datasets and generate actionable insights.
  

Preferred Qualifications:

• Experience in

  time series forecasting, anomaly detection, or optimization problems

  .
• Knowledge of cloud ML platforms (AWS Sagemaker, Azure ML, GCP AI Platform).
  
• Familiarity with MLOps concepts (model deployment, monitoring, CI/CD).
  
• Exposure to big data tools (Spark, Hadoop) is a plus.
  

Soft Skills:

• Strong communication and collaboration skills.
  
• Ability to explain technical concepts to non-technical stakeholders.
  
• Adaptability and eagerness to learn new techniques and tools.