Job Description
ROLE BRIEF
As an ML Engineer you will solve some of the most impactful business problems for our clients using a variety of AI and ML technologies. You will collaborate with business partners and domain experts to design and develop innovative solutions on the data to achieve predefined outcomes.
KEY RESPONSIBILITIES
- Engage with clients to understand current and future business goals and translate business problems into analytical frameworks
- Develop custom models based on in-depth understanding of underlying data, data structures, and business problems to ensure deliverables meet client needs
- Drive the development of cloud-based machine learning pipelines for data-driven products and services
- Create repeatable, interpretable and scalable models
- Promote a culture of self-serve data analytics by minimizing technical barriers to data access and understanding.
- Expertise in Monitor, optimize, and maintain ML solutions. Design data preparation and processing systems.
- Develop & Deployed ML Models, Automate and orchestrate ML pipelines.
- Effectively communicate the analytics approach and insights to a larger business audience
- Collaborate with team members, peers and leadership at Tredence and client companies
ESSENTIAL EXPERIENCE AND QUALIFICATIONS
1. Bachelor's or Master's degree in a quantitative field (CS, machine learning, mathematics, statistics) or equivalent experience.
2. 2-4 years of experience in data science, building hands-on ML models
- Experience leading the end-to-end design, development, and deployment of predictive modeling solutions.
4. Excellent programming skills in Python. Strong working knowledge of Pythons numerical, data analysis, or AI frameworks such as NumPy, Pandas, Scikit-learn, Jupyter, etc.
- Advanced SQL skills with SQL Server and Spark experience.
6. Experience with distributed computing frameworks (e.g. Hadoop, Spark), is a plus.
7. Knowledge of predictive/prescriptive analytics including Machine Learning algorithms (Supervised and Unsupervised) and deep learning algorithms and Artificial Neural Networks
8. Experience with Natural Language Processing (NLTK) and text analytics for information extraction, parsing and topic modeling.
9. Experience using Kubernetes or similar orchestration systems
- Excellent verbal and written communication. Strong troubleshooting and problem-solving skills. Thrive in a fast-paced, innovative environment
- Experience with data visualization tools — PowerBI, Tableau, R Shiny, etc. preferred
12. Experience with cloud platforms such as Azure, AWS is preferred but not required.