Data Science is the heart and core of our company. Algorithms and models are the foundation upon which our products are built, with data driving our keys, testing and growth. At Perengo (TMP), we are developing state-of-the-art large-scale recruitment advertising and machine learning applications. We are looking for talented scientists to support you. In this role you will work with other data scientists, engineers, and product managers on high impact initiatives.
- Perengo (TMP) is a programmatic job advertising platform.
- We automate job distribution, measure / analyze performance in real time, and optimize our customer's primary KPI's including cost-per-application and cost-per-hire.
Why Join Us?
- The company culture and engineering vision
- A chance to build something revolutionary
- The market is ripe for disruption and there's no doubt that whoever cracks the puzzle wants to win the market. This is also a rare opportunity for you to build a large-scale platform that is positively impacted by (instead of, say, boring old enterprise software that few people touch!).
- Quality of the Team - Truly high-performing teams are hard to come by; joining Perengo gives you an opportunity to collaborate with, learn from, and grow together with like-minded, successful tech veterans.
Who are we looking for:
- Knows and loves the startup world
- Quick thinking and acting with minimal / no supervision
- Able to build enterprise-grade software off of minimal and changing requirements
- Self-driven, independent, creative and eager to learn new skills
- Are not happy with, and build to great
- Building machine learning models to optimize campaign performance at massive scale, in particular understanding how bids / campaign features / texts (SEM) influence business metrics
- Run and analyze A / B tests and experiments for campaign feature changes / addition
- Exploratory analyzes of inform business strategies; identifying, creating, and transforming informative features based on historical and real-time data
- Working with Product to define data collection and modeling frameworks
- Coordinating and working closely with engineering models and algorithms into production
Requirements and Background
- Deep understanding of machine learning models (supervised / unsupervised / neural network) and the best use cases for them; NLP related tasks highly desirable.
- Knowledge in inferential statistics (classical / Bayesian), hypothesis-testing
- Experience in optimization (multivariable optimization with constraints, combinatorics)
- Experience building models and placing models into production
- Well versed in statistical programming languages such as R, Python
- Familiarity with relational databases search as Postgres and Amazon Redshift, and accessing data via SQL
- Computer science / engineering, operations research, decision sciences, discrete mathematics
- Graduate work in machine learning a plus, or relevant work experience
- Bonuses: Experience using scalable technology such as Spark, Using Markov chains / simulations, Experience in advertising "Ad Tech"
- Growth scientist
- Data Scientist, Advertising
- Machine Learning Engineer, Ad Platforms
Disability status, protected veteran status, or any other characteristic protected by law.