Machine Learning Engineer at Cerberus Capital Management in NYC
As a machine learning engineer your mission is to build & deploy the best analytical solutions hosted in hybrid cloud environments that have the capability to scale quickly & easily. Cerberus owns over 40 distressed debt companies that span a variety of industries. Our Data Science team works to identify & create new business values by reshaping the way our enterprise organization operate. As a Machine Learning Engineer, you will unlock the hidden patterns & trends optimizing business processes & increasing the top of line growth of our businesses to outperform competitors.
What You’ll Do:
- Predict future business outcomes of our portfolio companies by applying data mining techniques (both supervised & unsupervised learning)
- Connect & blend data from various data sources unlocking hidden patterns & trends within enterprise organizations (python, pandas, or SQL)
- Clean & structure data to eliminate redundant or unneeded information to facilitate reliable & robust analysis
- Choose analytical tools (segmentation, predictive models, personalization) based upon specific business problems across variety of industries (retail, industrial, manufacturing, telecom)
- Build predictive models that are conceptual & logical that support data-driven insights for deployment on modern data platforms (spark, Hadoop & other map-reduce tools).
- Partner with data engineers by proving requirements gathered during data discovery to ensure the right metrics (data model, architecture and infrastructure) are put in place when building data pipelines
- Develop containerized algos that are productionalized & deployed in hybrid cloud environments (GCP, Azure)
Who You Are:
- Quantitative minded and have:
- Knowledge of quantitative methods such as linear algebra, predictive analytics and machine learning algorithms
- Experienced in analyzing and exploring large datasets with Python, Pandas, SQL.
You are an engineer and have experience in:
- ETL process
- Data warehousing and data architecture concepts
- Data validation and quality assessment processes
- Model implementation
Ability to break down technical ideas and present them in business-friendly language
- Bachelors, Masters or PhD in statistics, math, physics, computer science, information systems or other quantitative disciplines.
- preferred to have experience working with one of the major cloud solutions (AWS, GCP and Azure) but not required