דרושים » תוכנה » Data Scientist

Requirements:
What Experience Will Drive to Success:
* Bachelors degree in data Science, Computer Science, Computer Engineering, or related field.
* 2+ years of experience in AI/ML engineering, with a proven track record of building and deploying ML & AI systems.
* Strong programming skills in Python and experience with ML frameworks such as TensorFlow, PyTorch, and Scikit-Learn.
* Hands-on experience with Big Data processing frameworks (Apache Spark, Dask, Ray).
* Hands-on experience with LLMs and AI Frameworks (LangChain, OpenAI, HuggingFace, LangGraph, etc.)
* Experience with Git and Jenkins.
* Strong problem-solving skills, a passion for innovation, and a self-driven mindset.
* Excellent communication and collaboration skills.
* Familiarity with AWS AI/ML services and cloud-based deployments (Advantage).
* Experience working with MLOps tools, including model registry, experiment tracking, and model serving (Advantage).
What We Are Looking For Independent and Self-Driven: Were building an AI Department from the ground up, so we need someone who is proactive, takes ownership, and thrives in a startup-like environment. Passion for Innovation: Whether its building scalable data infrastructure or creating advanced AI models, we are looking for someone passionate about solving complex challenges and delivering innovative solutions. Collaboration Skills: Appdome is all about teamwork. You will collaborate with engineers, data scientists, and product teams to build impactful solutions. Strong communication and interpersonal skills are key. Adaptability and Problem-Solving: Things move quickly at Appdome. You should be able to adapt to changing requirements and quickly solve problems in a fast-paced, dynamic environment. Can-Do Attitude: Were looking for motivated individuals who take the initiative, face challenges head-on, and can work independently while contributing to team success. About the Company: Appdome's mission is to protect every mobile app worldwide and its users. We provide mobile brands with the only patented, centralized, data -driven Mobile Cyber Defense Automation platform. Our platform delivers rapid no-code, no-SDK mobile app security, anti-fraud, anti-malware, anti-cheat, anti-bot implementations, configuration as code ease, Threat-Events threat-aware UI / UX control, ThreatScope Mobile XDR, and Certified Secure DevSecOps Certification in one integrated system. With Appdome, mobile Developers, cyber and fraud teams can accelerate delivery, guarantee compliance, and leverage automation to build, TEST, release, and monitor the full range of cyber, anti-fraud, and other defenses needed in mobile apps from within mobile DevOps and CI/CD pipelines.

Requirements:
5+ years of experience as a Data Scientist, working on machine learning-related projects or equivalent experience
Hands-on experience in modern AI techniques, including LLMs, embeddings, knowledge graphs, retrieval-augmented generation (RAG), multi-agent systems and prompt-engineering
Strong proficiency in Python and relevant ML/AI frameworks (e.g., Hugging Face Transformers, PyTorch, scikit-learn), as well as graph and vector database technologies
Experience working with large datasets and data processing libraries (e.g., Pandas, NumPy, Spark)
Pragmatic approach, open-minded, good communicator who can simplify complex ideas and designs
BSc in Computer Science or related field. MSc - advantage
Background in cybersecurity - advantage.

Requirements:
MSc in Computer Science, Engineering, or a related field
Focus on AI, ML, or DS.
5+ years of experience in DS roles, with a strong emphasis on building and deploying machine learning models in production environments.
Expertise in Python and SQL.
Experience with machine learning frameworks such as TensorFlow or PyTorch.
Familiarity with cloud platforms (e.g., AWS, GCP) and containerization tools (e.g., Docker, Kubernetes).
Proven experience in developing, fine-tuning, and deploying ML models, particularly in NLP.
Ability to translate complex business problems into data-driven solutions.
Excellent verbal and written communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.

Requirements:
At least 3 years of experience in product-oriented data analytics/feature engineering/data science/research /hypothesis lead research.
Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, etc.
Experience with productizing machine learning models and/or implementation of Data Pipelines.
Experience working with large and complex data sets.
Experience with Cloud services.
BSc in Computer Science/Software Engineering or equivalent knowledge.
Fast learning and ability to dive into new technologies and new pieces of code.
Enthusiastic and motivated to improve.
Independent thinker with a desire to learn.
Advantages
Experience with Kusto and DataBricks
Relevant masters degree
Proven experience in Cyber Security.
Experience in common Networking protocols.
Big Data (Hadoop, NoSQL, Elastic Search, Spark).

Requirements:
Reach out if you have:
Proven strong expertise (8+ years) as a data scientist and machine-learning researcher.
Proven strong expertise (8+ years) in Python, with a production-level design and coding skills and hands-on experience with coding machine learning / statistical modeling based solutions
M.Sc. in Computer Science, Mathematics, Engineering or a related field, with strong theoretical background in supervised & unsupervised modeling and various other statistical modeling techniques
High-level experience with databases approaches, such as SQL and document-oriented (e.g. MongoDB) and big data
Experience in data analysis and visualization
Strong problem solving and critical thinking skills
Can-do approach
Background in Cybersecurity - an advantage
Theoretical and practical background in Generative AI - an advantage.

Requirements:
At least 3-4 years of relevant experience and track record in Data Science: Machine Learning, Deep Learning, and Statistical Data Analysis.
Proven experience in developing and deploying production-level models within enterprise environments.
MSc or PhD degree in CS or Mathematics, Bioinformatics, Statistics, Engineering, Physics, or similar discipline.
Strong hands-on experience in Python with a focus on statistical algorithms development.
Experience with data science libraries such as: sklearn, pandas, numpy, pytorch/tensorflow.
Team player, able to work in collaboration with subject matter experts, with the ability to clearly present and communicate findings.
Proven ability to build and deliver data solutions in a short time frame.
Experience with AWS, Docker, and development methodologies - an advantage.
Active Github, Kaggle member an advantage.
Background in data loss prevention or related fields an advantage.

Requirements:
At least 4+ years of relevant experience and track record in Data Science: Machine Learning, Deep Learning and Statistical Data Analysis.
MSc or PhD degree in CS or Mathematics, Bioinformatics, Statistics, Engineering, Physics or similar discipline.
Strong hands-on experience in Python with the focus being on statistical algorithms development.
Experience with data science libraries such as: sklearn, pandas, numpy, pytorch/tensorflow
Team player, able to work in collaboration with subject matter experts, with ability to clearly present and communicate findings.
Proven ability to build and deliver data solutions in a short time frame.
Experience with AWS, docker, and development methodologies - an advantage
Experience with Snowflake and Databricks an advantage.
Active Github, Kaggle member an advantage.
Cyber security background an advantage.

Requirements:
Advanced degree (MSc required; Ph.D. strongly preferred) in Statistics, Machine Learning, or a closely related field, with at least 7 years of experience working in data science roles.
High proficiency in statistics, machine learning algorithms, data manipulation and analysis.
Extensive experience in research-oriented roles, analyzing business problems from a data science perspective, conducting literature research, and implementing and testing solutions
Expertise in supervised and unsupervised machine learning models (e.g., GLM, GAM, XGBoost, Deep Learning).
A proactive approach to staying updated with the latest developments in AI and incorporating them to enhance existing products.
A product-driven mindset with a knack for transforming research outcomes into impactful products.
Experience managing stakeholders and prioritizing across multiple business and technical needs.
Excellent communication skills, with the ability to articulate experiment goals and methodologies clearly and communicate insights effectively to the team.

Requirements:
- Bachelors degree in data Science, Computer Science, Computer Engineering, or related field. - 2+ years of experience in data Science roles. - Proven experience managing AI/ML project lifecycles from conception to deployment. - Deep understanding of classic ML, deep learning, and LLMs. - Strong proficiency in Python and experience with frameworks such as PyTorch, Scikit-Learn, and TensorFlow. - Hands-on experience with low-level programming in C / C ++. - Team player with strong collaboration and communication skills. - Quick learner with ability to adapt to new tools, methods, and frameworks. Preferred Qualifications: - Background in mobile app research or development ( Android / IOSgreenTxtBg!). - Image processing experience. - Reverse engineering experience with tools like IDA, gdb, and lldb. - Knowledge of OS internals. - Cybersecurity background with understanding of threat landscapes and attack vectors. About the Company:our company's mission is to protect every mobile app worldwide and its users. We provide mobile brands with the only patented, centralized, data -driven Mobile Cyber Defense Automation platform. Our platform delivers rapid no-code, no-SDK mobile app security, anti-fraud, anti-malware, anti-cheat, anti-bot implementations, configuration as code ease, Threat-Events threat-aware UI / UX control, ThreatScope Mobile XDR, and Certified Secure DevSecOps Certification in one integrated system. With our company, mobile Developers, cyber and fraud teams can accelerate delivery, guarantee compliance, and leverage automation to build, TEST, release, and monitor the full range of cyber, anti-fraud, and other defenses needed in mobile apps from within mobile DevOps and CI/CD pipelines. Leading financial, healthcare, m-commerce, consumer, and B2B brands use our company to upgrade mobile DevSecOps and protect Android & IOS apps, mobile customers, and businesses globally. Today, our company's customers use our platform to secure over 50,000+ mobile apps, with protection for over 1 billion mobile end users projected.
we are an Equal Opportunity Employer. We are committed to diversity, equity, and inclusion in our workplace. We do not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other characteristic protected by law. All qualified applicants will receive consideration for employment without regard to any of these characteristics.

Requirements:
MSc (or higher) in Statistics, Machine Learning, Applied Mathematics, or a related quantitative field.
4+ years of experience as a data scientist, with significant focus on insurance pricing or underwriting analytics.
Strong domain expertise in insurance pricing workflows, including rate plan development, evaluation, and optimization.
High proficiency in statistics, machine learning algorithms, data manipulation and analysis.
Extensive experience in research-oriented roles, analyzing business problems from a data science perspective, conducting literature research, and implementing and testing solutions.
Expertise in supervised and unsupervised machine learning models (e.g., GLM, GAM, XGBoost, Deep Learning).
A proactive approach to staying updated with the latest developments in AI and incorporating them to enhance existing products.
A product-driven mindset with a knack for transforming research outcomes into impactful products.
Excellent communication skills, with the ability to articulate experiment goals and methodologies clearly and communicate insights effectively to the team.