Machine Learning Developer

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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