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.