דרושים » דאטה » Senior Data Scientist ML Foundations

Requirements:
MSc in Computer Science/Engineering or related field with a focus on applied statistics, AI, machine learning, or related fields - a must.
6+ years of production environment experience, working with predictive and probabilistic models, clustering algorithms, classification models, search and recommendation techniques - a must.
Proven record for successful production implementation of translating product/business requirements into a technical solution.
Team player, Ability to handle several tasks simultaneously.
Strong knowledge in Python, SQL, and NoSQL.
Spark and Big data technologies experience an advantage.
Research experience - an advantage.
Experience with e-commerce - an advantage.

Requirements:
Masters in Computer Science/Statistics/Economics/Engineering or related field with a focus on applied statistics, AI, machine learning, or related fields
5+ years applying machine learning to real-world problems in an industrial setting.
Strong engineering and coding skills, with ability to write high performance production code. Ability to lead, envision and implement improvements of our ML solution and pipelines
Proficiency in Python, SQL, Spark.
Hands-on experience in Machine learning frameworks such as Scikit-learn, TensorFlow, Pandas, Numpy etc.
Strong understanding of evaluation methods for recommender systems and ability to run offline and online using experimental design
Preferred proficiency with data pipelines in Hadoop/Spark in a cloud environment
Ability to write and execute complex queries in SQL against different database architectures
Working knowledge of agile development processes and methodologies
Strong analytical & problem-solving skills, and excellent communication skills

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:
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:
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 hands-on experience developing and deploying machine-learning or data-science solutions with Python and SQL.
Proven, end-to-end experience building AI- and machine learning-based solutions from prototype to production deployment.
Demonstrated success shipping data-intensive services to production on cloud infrastructure (AWS preferred) using data tools such as PostgreSQL, Databricks, Spark, or Airflow.
Deep understanding of machine-learning fundamentals and practical expertise with frameworks such as TensorFlow, PyTorch, or scikit-learn.
Expertise in machine learning metrics and quality control.
Solid understanding of software-engineering best practices (including design patterns, data structures, and version control).
Excellent interpersonal and communication skills, with the ability to explain complex technical concepts to non-technical stakeholders and collaborate across teams.
Its even better if you have:
Experience with Agile development in fast-paced, delivery-driven environments.
Familiarity with CI/CD practices, containers, Kubernetes, and serverless or microservice architectures.
Experience with geospatial analysis, government data, survey research, or civic-tech applications.
A track record of contributing to open-source projects.
A college or graduate degree in a relevant field.

Requirements:
5+ years experience as a Data Scientist
2+ years experience working with Deep Learning.
Experience working with recommender systems - an advantage
Hands-on experience building and deploying deep learning models to production
High level scripting and programming skills in Python
Superior verbal, visual, and written communication skills to educate and work with cross functional teams on controlled experiments.
Team player, responsible, delivery-oriented
Troubleshooter, problem-solver who knows how to navigate trade-offs
Work experience as a data analyst - an advantage
At least a Master's degree in Computer Science, Math, Physics, Engineering, Statistics or other technical field.
Experience in ML over user personalization, ad-tech, or highly imbalanced data- an advantage.

Requirements:
4+ years of hands-on experience researching, developing, and delivering machine learning models in high-scale, production environments (Python preferred).
Strong background in analyzing large, complex datasets using SQL, Spark, or similar tools.
Deep experience with analytics, machine learning, and deep learning frameworks: Scikit-learn, SciPy, TensorFlow, Pandas, NumPy, PyTorch, Keras.
Demonstrated ability to solve multidisciplinary, data-driven problems.
Excellent teamwork and communication skills.
Experience in application or data security (strong advantage).
Experience with cloud platforms (AWS, Azure, GCP) is a plus.
Experience with Kubernetes (K8s) and microservices infrastructure is a plus.
Experience with CI/CD practices and the full lifecycle of ML model development, from data preparation to deployment and monitoring, is a strong advantage.
Ph.D. or M.Sc. in Computer Science, Engineering, Math, or another quantitative/technical field is valued, but not required if you have strong relevant professional experience.
Strong English and presentation skills are an advantage.

Requirements:
2 + years as a leader of engineering or data science teams, with a track record of shipping data-intensive tools or services to production.
6 + years hands‑on building data-driven solutions with Python and SQL.
Experience deploying services using cloud-based infrastructure and data tools such as PostgreSQL, Databricks, and Spark.
Strong understanding of the key concepts and usage of modern AI tools such as LLMs, RAG systems, and AI agents.
Solid software engineering fundamentals (including design patterns, data structures, and version control) and familiarity with Agile practices.
Proven ability to manage project scope, timelines, and cross‑team dependencies.
Comfort implementing rapid, iterative development processes while also prioritizing quality, reliability, and maintainability.
Excellent command of English, both verbal and written, and comfortable presenting technical topics to non‑technical stakeholders.
Its even better if you have:
Proficiency with CI/CD tools and practices, containers, serverless architectures, and automated testing and monitoring.
Experience deploying models to production using leading machine learning frameworks such as TensorFlow or PyTorch.
Experience with Agile development processes and project management in JIRA.
Expertise in machine learning metrics and monitoring.
Experience with geospatial analysis, government data, survey research, or civic tech applications.
Demonstrated success hiring and developing technical talent.
Contributions to open‑source projects or an advanced degree in a relevant field.

Requirements:
Education & Experience:
Bachelor's degree in Computer Science, Engineering, or a related STEM field; Masters in a data science-related field is a plus.
6+ years in a data science role, ideally within a SaaS or tech-driven company.
Proven experience leading and delivering AI-based solutions to key business problems.
Technical Skills:
Strong skills in data manipulation, visualization, and statistical analysis.
Proficiency with Python and the Python data stack.
Experience with ML frameworks: scikit-learn, XGBoost, PyTorch, TensorFlow.
Advanced SQL and experience working with large datasets.
Experience with LLMs and tools like LangChain, LlamaIndex.
Understanding of backend development architecture patterns an advantage.
Experience working with big data a plus.
Soft Skills:
Strong analytical thinking and independent problem-solving skills.
Excellent communication and collaboration abilities (Hebrew & English).
Self-driven with a high sense of ownership and motivation to deliver impactful solutions.
Creative, critical thinker and continuous learner.
Strong interpersonal skills with a proven ability to mentor and technically lead others.