Behavox is shaping the future for how businesses harness their most important raw material - data. Our mission is bold: Organize enterprise data into actionable information that protects and promotes the business growth of multinational companies around the world.
From managing enterprise risk and compliance to maximizing revenue and value, our data operating platform presents a widespread opportunity to build multilingual, AI/ML-based solutions that activate data for every function within a global enterprise.
The Data Analytics Team
creates applications that leverage tools provided by the Behavox Platform to give clients insights into their data. Specifically, we work with subject matter experts to learn about topics like insider dealing, and then, using linguistic rules, NLP, and machine learning, create models to detect this illegal activity within our clients' data. Each application is a small product itself - with its context, objectives, approach, means.
This will be and exciting opportunity for a talented individual looking for:
- Impact - you will see your contribution to Behavox product offering and consequently, on the business world.
- Autonomy, ownership, and professional growth.
- The opportunity to use cutting-edge technologies to solve practical problems on real data.
- Build data-driven and ML-based compliance / conduct solutions in Python
- Work on production applications
- Refine internal tools and automations
- Use statistics or NLP to get important insights
- Explore varied datasets to suggest and POC data and model improvements
- Fresh NLP experience (text analysis, theory and approaches of applied linguistics, NLP tools, statistical concepts related to text data analysis)
- First-hand experience in analyzing and adjusting ML models performance
- Python development, preferably related to ML or NLP (sklearn, pandas, matplotlib / pyplot, tensorflow / pytorch, spacy, nltk), in research or production setting, including git flow, collaboration, tests. Java / Groovy experience would make you even more valuable, but is not mandatory.
- Knowledge of statistics based on University level courses AND/OR applied within a working environment.
- Experience in consistently working on the same product during different stages of its lifecycle.