In this team you'll have a unique opportunity to have first-hand exposure to the strategy of the company in key security initiatives, especially in building scalable and robust, intelligent and privacy-safe, secure and product-friendly systems and solutions. Our challenges are not some regular day-to-day technical puzzles You'll be part of a team that's developing novel solutions to first-seen challenges of a non-stop evolvement of a phenomenal product eco-system. The work needs to be fast, transferrable, while still down to the ground to making quick and solid differences.
Responsibilities :
- Build machine learning solutions to respond to and mitigate business risks in eHealthcare products / platforms. Such risks include and are not limited to abusive accounts, fake engagements, spammy redirection, scraping, fraud, etc.
- Improve modeling infrastructures, labels, features and algorithms towards robustness, automation and generalization, reduce modeling and operational load on risk adversaries and new product / risk ramping-ups.
- Uplevel risk machine learning excellence on privacy / compliance, interpretability, risk perception and analysis.
Qualifications :
Master or above degree in CS, EE or other relevant, machine-learning-heavy majors.Solid engineering skills. Proficiency in at least two of : Linux, Hadoop, Hive, Spark, Storm.Strong machine learning background. Proficiency or publications in modern machine learning theories and applications such as deep neural nets, transfer / multi-task learning, reinforcement learning, time series or graph unsupervised learning.Ability to think critically, objectively, rationally. Reason and communicate in result-oriented, data-driven manner. High autonomy.#J-18808-Ljbffr