Forbes champions success by celebrating those who have made it, and those who aspire to make it. Forbes convenes and curates the most influential leaders and entrepreneurs who are driving change, transforming business and making a significant impact on the world. The Forbes brand today reaches more than 140 million people worldwide through its trusted journalism, signature LIVE and Forbes Virtual events, custom marketing programs and 32 licensed local editions in 71 countries. Forbes Media’s brand extensions include real estate, education and financial services license agreements. For more information, visit the Forbes News Hub or Forbes Connect.
As a data scientist at Forbes on the Business Intelligence team, you’ll have the opportunity to work on a variety of unique and interesting challenges. You'll be part of a highly effective team, who are leveraging data science to advance our business and drive revenue growth.
Responsibilities:
- Own the development of new and existing machine learning models with a focus on increasing revenue
- Current areas of focus include building audience segments for advertiser targeting, automated anomaly detection, forecasting and topic modeling
- Work with the sr. analyst and data engineer to create data products with a focus on providing insights to drive traffic
- Data mining of large structured and unstructured datasets to uncover insights
- Identify and develop new alternative data sources
What You Bring:
- 2+ years experience as a data scientist (preferably in the publishing industry)
- PhD, MS, in computer science, applied mathematics or other quantitative/computational discipline from a top Western university
- Advanced experience with Python & SQL. Experience with Spark, Dask and html a plus. Certification preferred
- 2+ years experience with popular machine learning open source libraries (pandas, numpy, and sklearn, Tensorflow/Keras, etc.), APIs, cloud computing (Google Cloud Platform preferred), databases (BigQuery preferred), Web Analytics (Google Analytics 360 preferred), GitHub
- Excellent understanding of traditional machine learning methods, feature engineering and data cleansing/analysis
- Hands on experience with natural language processing, regression, classification, clustering and time series forecasting. Familiarity with deep learning approaches a plus
- Experience putting machine learning models into production in a cloud environment and creating data products, preferably at scale
- Experience presenting findings to internal stakeholders at different levels
- Experience with Business Intelligence tools (Looker preferred)
- Knowledge of data visualization tools preferred (i.e.matplotlib, plotly)
- Experience in managing medium and large-scale analytics projects from end to end