Informacje o stanowisku

Ascendix Technologies is a software and services technology company founded in 1996 and headquartered in the United States, in Dallas, Texas.



The company has software development centers and branch offices in Poland, Portugal, and Ukraine, and distributed teams all over Europe, as we cooperate remotely since 2020.



We are a community of professionals committed to delivering second-nature solutions globally to digitize, optimize, and leverage processes. We specialize in building solutions for globally-focused businesses that look to leverage cloud computing, and commercial and open-source technologies for big data, search, applications, and cross-platform mobility. We have deep experience in Salesforce.com CRM platforms, and offer products, services, and productivity modules.



Were hiring a highly skilled and experienced Senior Data Science Engineer to join our project team, dedicated to serving our client in the transportation sector. The successful candidate will play a critical role in developing and implementing advanced software solutions for dynamic pricing management and network planning optimization. This position offers the opportunity to contribute to the cutting-edge of transportation technology, driving efficiency, and profitability.

Youll collaborate with solution architects, product owners, and engineers, and offer innovative solutions while serving as the top-level technical expert.



Responsibilities:

• Design, develop, and implement AI-based solutions to solve business problems and drive efficiency. Take ownership of AI roadmap creation and delivery. Initial focus areas include dynamic pricing management and network planning optimization for our digital transportation client.
• Apply machine learning expertise across various domains, adapting and developing algorithms to meet specific project requirements. This involves close collaboration with clients to understand their challenges and objectives.
• Stay at the forefront of machine learning, data science, and artificial intelligence research. Evaluate and adopt new technologies, methodologies, and approaches to enhance project outcomes and operational efficiency.
• Work closely with clients and internal teams to define project requirements, communicate progress, and deliver results. Act as the key technical advisor for machine learning, providing insights and recommendations to drive decision-making.

Requirements:

• 5+ years of experience in applying Data Science/Machine Learning.
• Understanding of ML algorithms (both theoretical and practical) with good knowledge of ML libraries: TensorFlow, PyTorch, scikit-learn, etc.
• Masters level degree in Machine Learning, Computer Science, Mathematics, Physics, or related fields.
• Strong Software Engineering skills, with experience in different programming languages and a good grasp of at least one language, ideally Python.
• Familiarity with data manipulation and analysis tools, such as pandas, NumPy, and SQL.
• Familiar with SQL/NoSQL databases. Understanding of data visualization and business intelligence tools, such as Power BI.
• Experience with cloud computing platforms (AWS, Google Cloud, Azure) is required.
• Flexibility to work on various projects across different industries, with the ability to quickly understand and adapt to new domains.
• Analytical and problem-solving skills: ability to analyze complex data, identify key insights, and develop innovative solutions to challenging problems.
• Good communication and presentation skills.
• Strong knowledge of data protection laws (GDPR, HIPAA) and their application in AI and machine learning projects.

Would be a plus:

• Masters degree or PhD in Computer Science, Mathematics, Statistics, or a related field.
• Experience with Azure AutoML solutions.
• Familiarity with the transportation or logistics industry, including dynamic pricing calculations, demand prediction, timetable optimizations, and route planning.
• Interest and knowledge in emerging technologies and methodologies in data science and AI, such as deep learning, reinforcement learning, and neural networks.

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