About SupportLogic
SupportLogic SX™ is a platform that elevates customer service experience by leveraging natural language processing (NLP) and machine learning (ML). The platform seamlessly integrates with your existing ticketing system (such as Salesforce Service Cloud, Zendesk, Microsoft Dynamics), reads all the comments in every ticket to extract key signals related to customer sentiment and churn, predicting outcomes and providing proactive recommendations. Customer Support and Success organizations use the platform to stay on top of how their customers feel about them thus improving customer relationships, products and operations.
We are a well-funded startup with investments from top tier investors in Silicon Valley (Sorenson Ventures, Sierra Ventures) and a customer list that is a who’s-who of Enterprise IT companies. We are privileged to have customers who are not only outspoken fans of our product but also prove it by renewing every year.
About SupportLogic
SupportLogic is a well-funded and highly disruptive SaaS platform company that enables innovative companies like Nutanix, HPE Aruba Networks, Qlik and Databricks to transform their Voice of the Customer (VoC) programs and harness true customer sentiment signals in real-time to proactively improve customer relationships, products and operations while decreasing churn and top-line revenue leakage. We hire big picture thinkers who can simultaneously roll up their sleeves and deliver with measurable impact. We dream in years, plan in months and execute in days. Our culture is honest, fast paced, collaborative and very down to earth.
Overview of Role
We are seeking a backend engineer to join our team and help us deliver innovative solutions to our customers. The role includes configuring, improving and automating deployments of our DS/ML models and managing system configurations. The ideal candidate will have coding experience with Python.
This is a full-time position based in Israel (remote + office in TLV).
How Your Work Will Support Our Growth
- Managing the environment that allows to onboard new customers
- Automating data science processes
- Configuring deployments of different environments
- Working collaboratively across functions
Requirements
- Python: Django, Pandas, numpy, scikit-learn, basic familiarity with REST API
- Working in Linux OS: Understanding OS principles and feeling comfortable working with shell scripts
- Databases: Relational and no-sql databases
- Automation: Experience with CI/CD, git and working on server-side environments
- Data Science Pipelines: End-to-end automation of DS/ML workflows
- Model Deployment: Configuring and deploying ML models in different environments
- Cloud Platforms (e.g., AWS, GCP, or Azure): Experience with cloud-based deployment
- Version Control: Git or similar tools for collaborative development
- Experience: At least 2 years in similar environments
Nice to Have
- Containers & Orchestration: Docker, Kubernetes (preferred)
- Java: An advantage
- Data Engineering Basics: handling and preprocessing structured and unstructured data
- Information retrieval: knowledge of information-retrieval principles and algorithms focusing on textual search methods, NLP algorithms, ranking and using deep neural networks is a big advantage.
Top Skills
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