דרושים » תוכנה » data Scientist בחברת סטארט-אפ בתחום ה-Fintech

we are seeking a skilled and passionate Data Scientist. At Windward, we are dedicated to creating a safer, more transparent, frictionless, and efficient maritime industry. Our mission is safeguarding maritime domains with AI technology that makes the right action easy, empowering responsible governance and secure global trade for a better world.
What will you do?

Develop and implement a variety of models (e.g., LLM, predictive models, classifiers, time series) on both public and proprietary data
Lead projects from ideation to production, ensuring solutions are effective in real-world scenarios and monitored for quality over time
Collaborate with product managers to understand problems and define solutions
Contribute to the research environment in order to improve the flexibility and quality of delivery

Were hiring an Optimization Algorithm Developer to own the algorithmic core of our cloud cost optimization solutions. Youll design optimization strategies, apply predictive and machine learning methods where they add value, and work with backend and data engineers to embed algorithms into production.

This is a hands-on role for someone who enjoys solving complex cost/performance trade-offs at scale and wants to see their algorithms drive tangible impact for customers.


Your ownership will include:

Develop and refine optimization algorithms for cloud usage and workload management.
Use statistical modeling, predictive techniques, and machine learning to analyze patterns and anticipate future behavior.
Deliver practical solutions that provide value early and can evolve into production-grade algorithms.
Define data and integration requirements; collaborate with backend engineers to operationalize solutions.
Establish clear evaluation methods (backtesting, offline experiments, KPIs) to measure performance.
Stay current with cloud economics, Kubernetes scaling practices, and FinOps trends.

As a Data Scientist, you will be a part of a large and innovative data science group, working closely with Dev teams and product managers, to create business value through Data Science and Machine Learning methods using cutting edge technology, on large and diverse datasets.

The Role consists of understanding customers' needs and product requirements, exploring data, identifying and defining functionality from conception to delivery, creating and improving algorithms and ML pipelines, building measurement tools, providing data visualizations, and more.

Deliver end-to-end ML products - including data processing, problem analysis, research, model development, validation and metrics, implemented in production and with online testing.

Responsibilities:
Data-driven insights. Help identify new products and opportunities for the company in diverse domains.
Develop Machine Learning models. Define requirements & combine data from a wide set of sources to develop ML/DL models that will automate and speed up our business processes.
End to end model management. Conduct in depth research, POC, write production-grade code and implement it (all the way to production) using our MLOps Infra to solve a diversity of business problems.

This is a senior leadership role focused on technical excellence and innovation. You will mentor a talented team of engineers and scientists, guide the end to end technical strategy from innovative research to production deployment and ultimately define the art of the possible for our most critical operational systems.

Responsibilities:

Lead the design and architecture of complex, end to end AI systems, guiding projects from initial conception and research through to production deployment and impact analysis.

Own the technical vision for the next generation of intelligent automation, creating patented technologies that directly impact Salesforce Field Service's core competitive advantage and market differentiation.

Provide technical leadership and set the strategic direction for the research and development of novel optimization and machine learning solutions.

Drive fast paced experimentation by independently or collaboratively prototyping solutions that showcase how new AI technologies can address real world business challenges. Collaborate with cross functional teams to translate prototypes into actionable solutions and define incremental delivery plans for implementation.

Mentor and guide a team of AI developers and scientists, fostering a culture of innovation, technical excellence, and continuous learning.

Stay at the forefront of academic and industry advancements by attending top tier conferences, networking with experts, and continuously learning about the latest in optimization, AI, and large language models (LLMs) to identify, evaluate, and apply emerging techniques that drive a significant competitive advantage.
JR314441

We are seeking a skilled ML Engineer to design, build, and deploy advanced ML solutions that drive analytics, anomaly detection, and data classification across enterprise-scale environments.
This role will focus on developing and optimizing models, including LLMs and prompt engineering pipelines, and ensuring seamless integration into production workflows spanning cloud-native, on-premises, and Databricks ecosystems.
Key Responsibilities :
Design, build, and deploy ML models for user behavior analytics, anomaly detection, and data classification across enterprise environments.
Collaborate with data scientists, software and data engineers to integrate ML models into production pipelines, cloud-native environments, on-premises, and Databricks workflows.
Develop, fine-tune, and evaluate LLMs and prompt engineering solutions for data classification, labeling, and threat analysis features.
Optimize models using techniques like distillation, quantization, and efficient data structures to boost performance and lower resource cost.
Build high-performance data inputs using embeddings, vector databases, and distributed training frameworks.
Manage model lifecycle and performance via MLOps best practices: monitoring, retraining, and deploying updates.
Partner with data scientists, cybersecurity researchers and product teams to evaluate and refine ML-driven capabilities.
Conduct experiments and benchmark results in fast-paced, data-intensive environments.