Develop, optimize, and deploy machine learning models and data products to address complex business challenges at the company
Requirements
- Strong proficiency in Python, including experience with libraries such as scikit-learn, TensorFlow, PyTorch, XGBoost, and HuggingFace.
- Hands-on experience with supervised and unsupervised learning techniques, including regression, classification, clustering, decision trees, and neural networks.
- Proficiency in model optimization, feature engineering, hyperparameter tuning, and evaluation metrics to ensure robust and accurate results.
- Understanding of LLM architectures, fine-tuning, prompt engineering, and context retrieval.
- Practical experience with ML Ops practices to streamline and scale machine learning workflows, including model deployment and monitoring.
- Familiarity with cloud platforms and tools for data processing, model training, deployment, and monitoring (e.g., Azure ML, MLflow)
- Experience with languages such as R, JavaScript, Java, etc. is a plus.
Responsibilities
- Develop and optimize machine learning models using a variety of techniques, including large language models, neural networks, tree-based algorithms, and statistical methods.
- Analyze complex datasets (structured, semi-structured, and unstructured) by applying feature engineering and statistical techniques to extract actionable insights for model development.
- Design and maintain end-to-end machine learning pipelines emphasizing modularity, reproducibility, and efficient retraining.
- Prototyping and developing domain-specific AI agents that can perform tasks such as information gathering, data extraction, and intelligent actions.
- Deploy models into production environments using ML Ops practices such as version control, logging, monitoring, and lifecycle management to ensure scalability, reliability, and performance.
- Perform data exploration, preprocessing, and visualization to uncover trends and clearly communicate findings to both technical and non-technical stakeholders.
- Collaborate with data engineers, software developers, and product owners to integrate machine learning solutions into business applications and cloud platforms.
Other
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
- 1-3 years of applied data science and machine learning.
- Strong ability to collaborate with cross-functional teams and clearly present complex technical concepts to technical and non-technical audiences.
- Self-Directed
- Strong Written and Verbal Communication Skills
