Data Scientist

Personetics

. Frame problems, review prior art, and analyze available data to assess whether ideas are viable and worth pursuing .

Posted 9/24/2026full-timeTel Aviv • IsraelJuniorMid-LevelWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Data Science with a focus on model design, feature engineering, and production deployment in a regulated environment. Proficient in Python and familiar with GenAI applications, capable of collaborating with cross-functional teams to drive model performance and business outcomes.

Highest-signal resume keywords

Data SciencePython ProgrammingFeature EngineeringGenAI Application BuildingModel Evaluation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Gradient-Boosted TreesDeep LearningLLM PipelinesClassical MLModel DesignModel MonitoringProduction InferenceTool UseMulti-Step WorkflowsOrchestration

Soft Skills

Clear Technical WritingCommunication

Tools & Technologies

PandasNumPyScikit-learnPyTorchAWSAzure

Industry Keywords

FinTechBanking

Tech Stack

Tools & technologies

AWSAzureCloudNumpyPandasPythonPyTorchScikit-Learn

About the role

Key responsibilities & impact

Frame problems, review prior art, and analyze available data to assess whether ideas are viable and worth pursuing
Perform feature engineering and model design on behavioral financial data using gradient-boosted trees, deep learning, LLM pipelines, or agentic flows
Lead preparation of model specifications, evaluation evidence, and risk documentation for production in a regulated environment
Support models through production review
Take solutions from proof of concept to live service with Engineering
Integrate models and conduct controlled roll-outs against real customer behavior
Monitor performance, drift, and business KPIs in production
Continuously improve models based on production results
Collaborate closely with Product and Engineering to move ideas from evidence through production

Requirements

What you’ll need

2–3 years hands-on as a Data Scientist in a product environment
Strong Python (Pandas, NumPy, scikit-learn, PyTorch)
Classical ML on tabular data — feature engineering, gradient-boosted trees, and model evaluation
Hands-on GenAI/LLM application building — RAG, prompt engineering, and evaluation of LLM-based systems
Experience building agentic systems — tool use, multi-step workflows, and orchestration
Clear technical writing, and the ability to explain a model to a non-technical audience
FinTech or banking experience (nice to have)
Transformer models — fine-tuning and production inference (nice to have)
Experience taking a model into production, including monitoring and post-launch iteration (nice to have)
Cloud platforms (AWS, Azure) (nice to have)