326 to 350 of 886 Permanent Algorithms Jobs

with a range of clients to understand their business needs, design appropriate AI solutions, and ensure successful deployment and integration. This will involve designing and developing AI models and algorithms, conducting data analysis and pre-processing to prepare datasets for AI model training, and providing training and support to clients on AI tools and best practices. This role would be More ❯

with a range of clients to understand their business needs, design appropriate AI solutions, and ensure successful deployment and integration. This will involve designing and developing AI models and algorithms, conducting data analysis and pre-processing to prepare datasets for AI model training, and providing training and support to clients on AI tools and best practices. This role would be More ❯

with a range of clients to understand their business needs, design appropriate AI solutions, and ensure successful deployment and integration. This will involve designing and developing AI models and algorithms, conducting data analysis and pre-processing to prepare datasets for AI model training, and providing training and support to clients on AI tools and best practices. This role would be More ❯

with a range of clients to understand their business needs, design appropriate AI solutions, and ensure successful deployment and integration. This will involve designing and developing AI models and algorithms, conducting data analysis and pre-processing to prepare datasets for AI model training, and providing training and support to clients on AI tools and best practices. This role would be More ❯

with a range of clients to understand their business needs, design appropriate AI solutions, and ensure successful deployment and integration. This will involve designing and developing AI models and algorithms, conducting data analysis and pre-processing to prepare datasets for AI model training, and providing training and support to clients on AI tools and best practices. This role would be More ❯

with a range of clients to understand their business needs, design appropriate AI solutions, and ensure successful deployment and integration. This will involve designing and developing AI models and algorithms, conducting data analysis and pre-processing to prepare datasets for AI model training, and providing training and support to clients on AI tools and best practices. This role would be More ❯

with a range of clients to understand their business needs, design appropriate AI solutions, and ensure successful deployment and integration. This will involve designing and developing AI models and algorithms, conducting data analysis and pre-processing to prepare datasets for AI model training, and providing training and support to clients on AI tools and best practices. This role would be More ❯

with a range of clients to understand their business needs, design appropriate AI solutions, and ensure successful deployment and integration. This will involve designing and developing AI models and algorithms, conducting data analysis and pre-processing to prepare datasets for AI model training, and providing training and support to clients on AI tools and best practices. This role would be More ❯

world of sports analytics. Key responsibilities for the role of Data Scientist include: Collect, clean, and process football-related data from various sources. Develop and implement statistical models and algorithms to analyze player performance and match outcomes. Create detailed reports and visualizations to communicate insights and recommendations to technical and non-technical stakeholders. Collaborate with football analysts, coaches, and other More ❯

Proficiency with tools such as Burp Suite, OWASP ZAP, Nessus, Metasploit, Wireshark, Nmap, Fortify, Checkmarx. Knowledge of scripting languages such as Python, JavaScript, Bash, or PowerShell. Understanding of encryption algorithms, key management, and secure protocols (TLS, SSH, etc.) Detailed understanding of common vulnerabilities (e.g., OWASP Top 10, CWE/SANS Top 25). Familiarity with Linux, Windows, and network protocols More ❯

functional requirements. Co-design, pair on, and review the development work of other lead software developers. You’ll work on the entire company's backend code base: data models, algorithms, data pipelines, infrastructure, APIs, complex database queries, ML models. Learn, use, and improve best practices and processes for high-quality software development. Our technology stack includes Python 3.11, PostgreSQL, Django More ❯

functional requirements. Co-design, pair on, and review the development work of other lead software developers. You’ll work on the entire company's backend code base: data models, algorithms, data pipelines, infrastructure, APIs, complex database queries, ML models. Learn, use, and improve best practices and processes for high-quality software development. Our technology stack includes Python 3.11, PostgreSQL, Django More ❯

of audiences Ability to manage multiple projects and meet deadlines. Programming skills in Python, R, or other analytical tools (nice to have) Familiarity with statistical methods and machine learning algorithms (nice to have) Why you’ll love life at Veran Veran Performance offers a dynamic and inclusive work environment that fosters growth, collaboration, and innovation. As a team, we are More ❯

of audiences Ability to manage multiple projects and meet deadlines. Programming skills in Python, R, or other analytical tools (nice to have) Familiarity with statistical methods and machine learning algorithms (nice to have) Why you’ll love life at Veran Veran Performance offers a dynamic and inclusive work environment that fosters growth, collaboration, and innovation. As a team, we are More ❯

models at scale. At least 2 years of leadership experience managing ML Scientists or Engineers. Deep expertise in search and recommendation systems (e.g., semantic embeddings, learning-to-rank, personalisation algorithms). Hands-on experience with modern ML toolchains — Python, Spark, and frameworks such as PyTorch or TensorFlow. Strong grounding in experimental design, A/B testing, and the use of More ❯

models at scale. At least 2 years of leadership experience managing ML Scientists or Engineers. Deep expertise in search and recommendation systems (e.g., semantic embeddings, learning-to-rank, personalisation algorithms). Hands-on experience with modern ML toolchains — Python, Spark, and frameworks such as PyTorch or TensorFlow. Strong grounding in experimental design, A/B testing, and the use of More ❯

models at scale. At least 2 years of leadership experience managing ML Scientists or Engineers. Deep expertise in search and recommendation systems (e.g., semantic embeddings, learning-to-rank, personalisation algorithms). Hands-on experience with modern ML toolchains — Python, Spark, and frameworks such as PyTorch or TensorFlow. Strong grounding in experimental design, A/B testing, and the use of More ❯

models at scale. At least 2 years of leadership experience managing ML Scientists or Engineers. Deep expertise in search and recommendation systems (e.g., semantic embeddings, learning-to-rank, personalisation algorithms). Hands-on experience with modern ML toolchains — Python, Spark, and frameworks such as PyTorch or TensorFlow. Strong grounding in experimental design, A/B testing, and the use of More ❯

models at scale. At least 2 years of leadership experience managing ML Scientists or Engineers. Deep expertise in search and recommendation systems (e.g., semantic embeddings, learning-to-rank, personalisation algorithms). Hands-on experience with modern ML toolchains — Python, Spark, and frameworks such as PyTorch or TensorFlow. Strong grounding in experimental design, A/B testing, and the use of More ❯

models at scale. At least 2 years of leadership experience managing ML Scientists or Engineers. Deep expertise in search and recommendation systems (e.g., semantic embeddings, learning-to-rank, personalisation algorithms). Hands-on experience with modern ML toolchains — Python, Spark, and frameworks such as PyTorch or TensorFlow. Strong grounding in experimental design, A/B testing, and the use of More ❯

models at scale. At least 2 years of leadership experience managing ML Scientists or Engineers. Deep expertise in search and recommendation systems (e.g., semantic embeddings, learning-to-rank, personalisation algorithms). Hands-on experience with modern ML toolchains — Python, Spark, and frameworks such as PyTorch or TensorFlow. Strong grounding in experimental design, A/B testing, and the use of More ❯

models at scale. At least 2 years of leadership experience managing ML Scientists or Engineers. Deep expertise in search and recommendation systems (e.g., semantic embeddings, learning-to-rank, personalisation algorithms). Hands-on experience with modern ML toolchains — Python, Spark, and frameworks such as PyTorch or TensorFlow. Strong grounding in experimental design, A/B testing, and the use of More ❯

models at scale. At least 2 years of leadership experience managing ML Scientists or Engineers. Deep expertise in search and recommendation systems (e.g., semantic embeddings, learning-to-rank, personalisation algorithms). Hands-on experience with modern ML toolchains — Python, Spark, and frameworks such as PyTorch or TensorFlow. Strong grounding in experimental design, A/B testing, and the use of More ❯

models at scale. At least 2 years of leadership experience managing ML Scientists or Engineers. Deep expertise in search and recommendation systems (e.g., semantic embeddings, learning-to-rank, personalisation algorithms). Hands-on experience with modern ML toolchains — Python, Spark, and frameworks such as PyTorch or TensorFlow. Strong grounding in experimental design, A/B testing, and the use of More ❯

models at scale. At least 2 years of leadership experience managing ML Scientists or Engineers. Deep expertise in search and recommendation systems (e.g., semantic embeddings, learning-to-rank, personalisation algorithms). Hands-on experience with modern ML toolchains — Python, Spark, and frameworks such as PyTorch or TensorFlow. Strong grounding in experimental design, A/B testing, and the use of More ❯