Senior Data Scientist (Digital Twin)

Azərbaycan Yerüstü Nəqliyyat Agentliyi
5 Hacı Məmmədov, Azərbaycan
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Təsvir

Design, implement, and optimize advanced data processing workflows on large-scale datasets using Python, with a strong emphasis on data-wrangling libraries (such as pandas and numpy) and geo-analytics libraries (such as geopandas and shapely)
Lead the identification, acquisition, validation, and integration of raw datasets to enhance model performance and business insights
Develop, deploy, and continuously refine predictive models and machine learning algorithms to support travel demand forecasting and strategic decision-making
Define, calibrate, and optimize key parameters for demand forecasting systems, ensuring model robustness and accuracy
Support the modeling team in the setup, execution, and analysis of simulations across multiple demand and service scenarios, integrating geo-spatial analytics into the workflow and producing high-quality outputs for GIS-based decision-support tools
Lead scenario modeling and sensitivity analyses to assess the impact of different mobility policies and service configurations on transit demand
Collaborate on the development and enhancement of public transport network design and scheduling optimization models (including timetabling solutions)
Conduct advanced exploratory data analysis and develop impactful visualizations and dashboards using tools such as Tableau and Python visualization libraries (e.g., Plotly, Seaborn, Matplotlib)
Act as a data science advisor to cross-functional teams, translating business needs into analytical solutions and ensuring data-driven decision support

Tələblər

Advanced proficiency in Python programming (or C/C++), with deep knowledge of Python data manipulation libraries (pandas, geopandas) and scientific computing and machine learning libraries (such as Scikit-learn, SciPy, LightGBM, and XGBoost)
Demonstrated practical experience in data cleansing, transformation, advanced analytics, and data visualization, using tools such as Tableau or Python-based visualization frameworks
Strong expertise in statistical methods and quantitative problem-solving, with practical experience in developing demand forecasting models using techniques such as regression analysis, discrete choice modeling, and advanced curve fitting on real-world datasets
Solid experience in network design and scheduling optimization, with practical application of network flow models, linear programming (LP), and mixed-integer programming (MIP) methodologies
Good understanding of parallel and distributed computing principles for handling large-scale data processing and model training tasks
Hands-on experience with geo-spatial analytics, GIS tools, and familiarity with transport demand modeling and simulation software (e.g., PTV Visum, Aimsun) is considered a strong advantage
In-depth knowledge of the transportation and mobility industry, with the ability to translate complex business and operational challenges into data-driven analytical solutions
Excellent communication and collaboration skills, with the ability to work effectively in cross-functional and multidisciplinary teams
Fluency in English is mandatory; proficiency in Russian is considered an asset
Advanced Degree (Master's or Ph.D. preferred) in quantitative science such as Data Science, Statistics, Operations Research, Computer Science, or Industrial Engineering
At least 3–5 years of hands-on experience in data science, advanced data analytics, or predictive modeling, preferably applied to transportation, mobility, or network optimization domains