לוח משרות דרושים Senior Data Scientist

Requirements:
What It Takes 4+ years of hands-on experience in fraud analytics, data science, or risk modeling - Must Proven experience in the Fintech/Fraud Prevention Domain - Must
* Proven ability to take projects from A to Z in building data infrastructure, anomaly detection, and graph-based fraud detection solutions - Must
* Strong practical proficiency in Python, SQL, MLOps and fraud-related data tools - Must
* Strong background in e-commerce or payments - Advantage
* A self-starter mentality someone who identifies opportunities, takes initiative, and drives improvements with minimal direction. Our Story Chargeflow is a leading force in fintech innovation, tackling the pervasive issue of chargeback fraud that undermines online businesses. Born from a deep passion for technology and a commitment to excel in eCommerce and fintech, we've developed an AI-driven solution aimed at combating the frustrations of credit card disputes. Our diverse expertise in fintech, eCommerce, and technology positions us as a beacon for merchants facing unjust chargebacks, supported by a unique success-based approach. Propelled by nearly $20 million funding round led by OpenView Venture Partners and key fintech investors, Chargeflow has embarked on a product-led growth journey. Today, we represent a tight-knit community of passionate individuals and entrepreneurs, united in our mission to revolutionize eCommerce and fight against chargeback fraud, marking us as pioneers in protecting online business revenues.

Requirements:
What You Bring:
5+ years of experience in data science, with a strong foundation in statistics, Machine Learning, and business analytics. Hands-on experience in NLP, including training and deploying models. Proficiency in Python and ML frameworks such as scikit-learn, PyTorch, and Hugging Face. Experience with Azure ML and Amazon Bedrock environments. Bonus: Familiarity with Dify and Flowise for LLM orchestration.

Requirements:
Masters/Ph.D. in Computer Science, Electrical Engineering, Machine Learning, information systems engineering, or related field.
5+ years of hands-on experience with developing & maintaining production class Machine Learning projects aimed towards solving business problems.
Specialized expertise in Natural Language Processing (Transformers, LLMs).
Experience with Generative AI, for example: Prompt Engineering, Automatic Evaluation, Retrieval-Augmented Generation (RAG) and open-source LLMs Fine-Tuning a strong advantage.
Experience in data analysis and visualization and strong knowledge in SQL.

Requirements:
At least 5 years of experience in data science, solving real-world problems, and using modern technologies to develop ML models.
At least 5 years of experience with the Python data science toolbox (Pandas, NumPy, SKLearn, etc.).
Experience with the full lifecycle of a data product ideation to productionizing and iterating.
Experience with training, fine-tuning, and deploying LLMs, as well as applying state-of-the-art architectures to domain-specific challenges.
Experience with NLP algorithms and approaches and their applications to taxonomies, recommendations, and search domains.
Solid programming skills to code, build, and test models.
Knowledge of and appetite for mathematical principles that are the foundation of ML methods.
Familiarity with transformer-based architectures (e.g., BERT, GPT) and their application to classification and predictive modeling.
Experience in optimizing and scaling deep learning models for real-world deployment in production environments.

Requirements:
Masters degree in Statistics, Mathematics or Computer Science.
Minimum of 4 years of experience in designing, developing and deploying production-level deep learning recommendation models with a proven business impact.
Fluency in Python, Pandas/Dask, SQL, PyTorch or Tensorflow. Ability to write readable and maintainable code.
Strong communication and storytelling skills with both technical and non-technical audiences. Ability to present complex technical subjects to non-technical stakeholders.
Ability to read AI research publications and implement the algorithms & architectures from scratch.
Advantages
Advanced knowledge in generative models: Auto-encoding, adversarial models, compression.
Worked on deep learning graph model solutions with 10s of TB of data.
Publication in peer-reviewed conferences or journals on reinforcement learning, deep learning, and machine learning.
Strong passion for machine learning and investing independent time towards learning, researching, and experimenting with new innovations in the field.
Experience working with technologies like SageMaker, Athena/Trino, Spark, Milvus, and OpenSearch.

Requirements:
10+ years of experience in data-related roles, with at least 5 years in a leadership position (ideally within a tech or AI-driven startup environment).
M.Sc. or PhD in Data Science/Computer Science/Engineering/Statistics, or a related field.
Extensive experience with cloud platforms (AWS, GCP, or Azure) and modern data warehouses (Snowflake, BigQuery, or Redshift).
Proficiency in data technologies, such as SQL, Python, R, Looker and big data tools (e.g., Hadoop, Spark).
Proven experience in leveraging data for product development, business intelligence, and operational optimization.
Strong track record of building and managing cross-functional data teams and influencing across all levels of an organization.
Excellent communication skills, with the ability to convey complex data insights in an accessible manner to non-technical stakeholders.

Requirements:
4+ years in C++ and Python - Must.
Experience with low-level research and development: operating systems, embedded systems or equivalent experience - Must
Experience working with large open source projects is a plus
Experience with native mobile development is a plus
Team player as well as the ability to be independent and proactive.
BSc Degree or equivalent military experience.

Requirements:
5+ years of professional experience in Data Science - research, data analysis and implementation of predictive models
Experience with Machine Learning: Proven track record in productization of ML models
Proficiency in Python: Strong coding skills with a focus on writing efficient and scalable ML code
Production Experience: Demonstrated ability to deploy ML models in production, ensuring reliability, scalability, and real-time processing
Technologies: In particular AWS, Airflow and Software container technology - Docker, Kubernetes
B.Sc/M.Sc in a scientific field - Computer Science, Software Engineering, Data Science
Initiator and Promoter: Self-starter who actively proposes new ideas and promotes innovative solutions
Learner: Passion for continuous learning, staying updated on the latest SW and ML trends, tools, and research
Flexible Mindset: Adaptability in approaching problems and adjusting models or strategies as needed.

Requirements:
3+ years experience as team lead
5+ years experience as a Data Scientist
Proven experience delivering to production AI/ML systems (LLMs, conversational AI, or agent-based systems preferred), including concepts such as prompt engineering, agent orchestration, and RAG
Proven Experience with model evaluation, A/B testing, monitoring frameworks
Experience leading DS, Analytics & AI engineers in cross functional teams
Strong SE skills & experience in developing production level Python code
Ability to work across disciplines (Product, Engineering, Data) and communicate technical ideas clearly
Preferred Qualifications:
Experience in clinical, healthcare, or regulated environments
Experience delivering agents using LangGraph/LangChain

Requirements:
Solid experience in data science, with a focus on image analysis and text reading.
Proficiency in programming languages such as Python and familiarity with relevant libraries (e.g., TensorFlow, PyTorch, OpenCV).
Strong understanding of statistical modeling, machine learning, and deep learning algorithms.
Experience working with large-scale datasets and applying data preprocessing techniques.
Excellent problem-solving skills and the ability to work independently as well as in a team environment.
Strong communication and presentation skills, with the ability to convey complex concepts to non-technical stakeholders.

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:
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:
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.
Excellent communication skills, with the ability to articulate experiment goals and methodologies clearly and communicate insights effectively to the team.