Machine Learning Developer

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We are seeking a highly skilled and experienced Machine Learning Developer to join our dynamic

team. In this role, you will design, implement, and optimize machine learning models that drive

data-driven decision-making and improve business outcomes. You will work closely with cross-functional teams to solve complex problems and deliver scalable, high-performance solutions.


Key Responsibilities

  • Develop risk related models such as Application Scorecards, Behavioral Scorecard, Collection Scorecards and Fraud-related Scorecards
  • Analyze large datasets to identify patterns, trends, and actionable insights.
  • Implement scalable machine learning pipelines and integrate them into production systems.
  • Optimize model performance through feature engineering, hyperparameter tuning, and model evaluation.
  • Collaborate with data engineers to ensure efficient data processing and model deployment.
  • Stay up to date with the latest advancements in machine learning and data science to propose and implement innovative solutions.
  • Document workflows, processes, and results to ensure reproducibility and scalability of models.


Required Qualifications

  • Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related field.
  • 2 years' of hands-on experience in developing and deploying machine learning models.
  • Strong programming skills in Python, R, or similar languages, with experience in libraries like TensorFlow, PyTorch, or scikit-learn
  • Proficiency in data processing frameworks such as Pandas, NumPy, and Spark.
  • Experience with cloud platforms (e.g., AWS, GCP, Azure) and tools for model deployment (e.g., Docker, Kubernetes).
  • Strong understanding of algorithms, data structures, and statistical methods.
  • Experience with SQL and NoSQL databases for data manipulation and storage.
  • Excellent problem-solving skills and ability to translate business challenges into technical solutions.


Preferred Qualifications

  • Experience with deep learning techniques and frameworks.
  • Familiarity with big data tools such as Apache Kafka, Hadoop, or similar.
  • Knowledge of A/B testing, experimentation, and causal inference.
  • Experience with advanced visualization tools like Tableau, Power BI, or Matplotlib.
  • Strong communication skills and ability to present technical concepts to non-technical
  • stakeholders.
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