Ascendix Technologies, founded in 1996, is a global software service company specializing in AI and PropTech. Our core mission is to help our clients get top-notch business results through the smooth automation of processes, leveraging AI solutions, customer-facing apps, and precisely tailored back-office software to meet their distinct needs. Based in bustling Dallas, Texas, we operate across the globe, with strategically positioned teams throughout Europe.
We are looking for a Middle/Senior Data Science Engineer to join one of our international projects focused on optimization, planning, and decision-support systems. This role is ideal for someone who enjoys solving complex operational problems, building production-grade Python solutions, and working closely with cross-functional teams to deliver robust, scalable systems.
Requirements:
- 5+ years of commercial experience in applied Data Science, Machine Learning, or optimization-focused software engineering.
- Strong Python skills, including modern Python practices, type hints, packaging, and virtual environments.
- Hands-on experience with numerical and data analysis libraries such as NumPy and pandas.
- Experience with mathematical optimization or combinatorial optimization in production environments.
- Practical knowledge of optimization tools such as Google OR-Tools CP-SAT, or similar solvers like Gurobi, CPLEX, PuLP, or Pyomo.
- Experience designing and validating optimization or simulation models for scheduling, routing, capacity planning, or similar use cases.
- Solid understanding of statistics and demand modeling.
- Experience with asynchronous and service-oriented Python applications.
- Strong analytical and structured problem-solving skills.
- Ability to explain complex technical concepts to non-technical stakeholders.
- Good written and verbal English communication skills.
Nice to have:
- Background in Operations Research, Applied Mathematics, Industrial Engineering, or another quantitative field.
- Experience in transportation, mobility, logistics, or other operational planning domains.
- Experience tuning large-scale combinatorial or constraint-based optimization models.
- Familiarity with demand forecasting or pricing-related models.
- Knowledge of queue-based or task-based architectures.
- Experience with CI/CD pipelines and quality gates for analytical services.
- Familiarity with data privacy and compliance considerations in operational data.
- Exposure to additional ML techniques relevant to planning and optimization systems.
Responsibilities:
- Design and implement optimization solutions for complex planning scenarios.
- Translate business and operational requirements into mathematical models, constraints, and objective functions.
- Develop and maintain production-grade Python modules, services, and integrations.
- Validate, test, debug, and improve optimization and data-processing workflows.
- Profile and improve model correctness, performance, and solution quality.
- Collaborate with engineering, product, and business stakeholders to clarify requirements and refine solutions.
- Document algorithms, assumptions, and technical decisions in a clear and structured way.
- Continuously improve modeling approaches, methodologies, and engineering practices.
Ascendix offers you:
Healthcare:
Career and professional growth:
- B2B and remote cooperation
- Career development and professional growth
- Technical performance reviews and 360 Evaluation
- Collaboration with a personal Direct Manager, Technical leads, and mentors
Learning and Development:
- Udemy and Pluralsight subscription
- Internal knowledge-sharing opportunities
Corporate spirit:
- Online and offline events & team-buildings
- Friendly and supportive atmosphere
Please note
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We sincerely appreciate each response and the interest you have shown in our job opening. If we do not contact you within a week, it means that, at this moment, your CV has not been selected for further consideration for this position. Nevertheless, we will keep your resume in our candidate database and will definitely inform you if a vacancy that matches your experience becomes available.
