לוח משרות דרושים הייטק דאטה

Requirements:
Requirements:
4+ years of experience as a Software Engineer, including design & development.
Proven experience with Java or Go.
Experience in the design and development of scalable big data solutions.
Experience working in a cloud-based environment.
Passionate about technologies, frameworks, and best practices.
Ability to work in a fast-paced environment.

Advantages:
Experience with Spring / Kubernetes.
Experience with Terraform / Helm / Argo.

Requirements:
1. Technical Expertise
Cloud Data Platforms: Expertise in Google Cloud Platform (GCP)
services, especially in data-related services like BigQuery, Cloud SQL,
Dataproc, Pub/Sub, and Dataflow.
Data Engineering: Strong knowledge of data pipelines, ETL/ELT
processes, and data integration using GCP tools.
Database Management: Experience with both relational and
non-relational databases, including MySQL, PostgreSQL, and NoSQL
solutions like Firestore or Bigtable.
Programming Skills: Proficiency in Python, SQL, and possibly other
languages like Java or Scala, with a focus on data manipulation and
processing.
APIs and Integrations: Experience working with Google APIs and
third-party APIs for data extraction and integration.
Machine Learning: Familiarity with Google Cloud AI tools, such as
Vertex AI, and how to integrate machine learning workflows into the data
architecture.
Data Governance: Knowledge of data security, privacy, governance, and
regulatory requirements (GDPR, HIPAA, etc.).
2. Leadership and Management

Team Management: Ability to lead a team of data engineers, analysts,
and architects, providing guidance, mentorship, and performance
management.
Project Management: Strong skills in managing data projects, ensuring
timely delivery while meeting client requirements.
Collaboration: Experience working cross-functionally with stakeholders
such as solution architects, developers, and business teams to translate
business needs into data solutions.
Resource Allocation: Efficiently managing cloud resources to optimize
cost and performance.
3. Client-Facing Skills
Consulting: Experience in providing consulting services to clients,
including understanding their data needs, providing solutions, and
guiding them through the data modernization process.
Pre-sales Support: Supporting sales teams in scoping out client
projects, preparing presentations, and explaining technical aspects of
data solutions during pre-sales.
Client Relationship Management: Building and maintaining strong
relationships with clients to ensure satisfaction and long-term
partnership.
4. Certifications and Education
● Google Cloud Certifications:
● Professional Data Engineer (preferred)
● Professional Cloud Architect (preferred)
● Other Certifications: Optional certifications in data engineering or
machine learning from organizations like Cloudera, AWS, or Microsoft
Azure can be advantageous.
● Educational Background: Bachelors or Masters degree in Computer
Science, Data Science, Engineering, or a related field.
5. Soft Skills
● Problem-Solving: Ability to troubleshoot data-related issues and
implement scalable solutions.
● Adaptability: Ability to keep up with the rapidly changing cloud and data
landscape.
● Communication: Excellent verbal and written communication skills to
explain complex data systems to non-technical stakeholders.

Requirements:
Proven experience as a Solution Architect or in a similar role, with a strong focus on DataOps.
Deep understanding of data management principles, data modeling, and ETL processes.
Expertise in cloud technologies (AWS, Azure, GCP) and data platforms (Hadoop, Spark, Snowflake, Looker).
Strong knowledge of programming languages (Python, Java, Scala) and scripting languages (Bash, PowerShell).
Experience with DevOps tools and practices (CI/CD, containerization, orchestration).
Excellent communication and collaboration skills, with the ability to work effectively with both technical and non-technical stakeholders.
Strong problem-solving and analytical skills
Bachelors degree in Computer Science, Engineering, or a related field.

Requirements:
7+ years of experience in data science, with a strong focus on NLP and/or Generative AI.
Proven expertise in developing and deploying machine learning models at scale in production environments.
Strong proficiency in Python, SQL, and ML frameworks.
Hands-on experience with big data technologies and cloud services, especially for processing and analyzing large-scale datasets.
Experience with MLOps tools and cloud-based AI infrastructure.
Excellent collaboration and communication skills, with a track record of working cross-functionally with engineering and product teams.
Nice to have:
Prior experience in the data security or the compliance industry.
Experience processing large amounts of data using Big Data tools like Spark.
Experience using cloud based fine tuning frameworks.
A Masters or Ph.D. in a relevant field (e.g., Data Science, Computer Science, Statistics, Mathematics) is preferred.

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:
Leadership Experience: 3+ years of hands-on experience managing BI/Data Engineering teams, including hiring, mentoring, and performance management.
Technical Expertise: 5+ years of experience building scalable Big Data pipelines, ETL workflows, and data models across cloud and on-premises platforms.
Programming Skills: 3+ years of proficiency in Python or equivalent programming languages for data manipulation, automation, and service integration.
Visualization Proficiency: Proven ability to translate complex datasets into impactful dashboards and insights using Tableau, Imply, or similar tools.
Project Management: Strong organizational skills with the ability to prioritize and manage multiple tasks and projects in parallel.
Communication: Excellent verbal and written communication skills, with the ability to explain technical 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.

Requirements:
Have 5+ years of hands-on experience in designing and maintaining big data pipelines across on-premises or hybrid cloud environments, with proficiency in both SQL and NoSQL databases within a SaaS framework.
Proficient in one or more programming languages: Python, Scala, Java, or Go.
Experienced with software engineering best practices and automation, including testing, code reviews, design documentation, and CI/CD.
Experienced in building and designing ML/AI-driven production infrastructures and pipelines.
Experienced in developing data pipelines and maintaining data lakes on AWS - big advantage.
Familiar with technologies such as Kafka, Snowflake, MongoDB, Airflow, Docker, Kubernetes (K8S), and Terraform - advantage.
Bachelor's degree in Computer Science or equivalent experience.
Strong communication skills, fluent in English, both written and verbal.
A great team player with a can-do approach.

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:
3+ years of direct experience in BI Development; writing complex and efficient Big Query queries.
2+ years of experience with BI tools (preferably Power BI).
Bachelors degree in computer science or related field.
Demonstrated advanced analytical, multi-tasking and problem-solving skills.
Direct experience with financial analysis a plus.

Requirements:
5+ years of experience in Data Science. Experience in developing ML / DL / LLM / NLP algorithms.
Bachelors or Masters degree in Data Science, Computer Science, Statistics, or a related field, with a significant focus on machine learning.
High proficiency in Python.
Excellent understanding of machine learning techniques, algorithms, and methodology.
Experience with common data science toolkits, such as Pandas, Scikit-learn, Tensorflow, and more.
High-level applied statistics skills, such as distributions, statistical testing, etc.
Experience in data mining and insight drawing from large data sets.
Data-oriented personality, analytical mind, and problem-solving aptitude.
Experience in Constraint programming or Network science - an advantage.
A team player with a can-do attitude, delivery-oriented.