As a Data Scientist specializing in map data and point of interest (POI), you will play a crucial role in leveraging data science techniques and algorithms to extract insights, optimize processes, and enhance the accuracy and relevance of our mapping data and POI information. You will be responsible for analyzing large-scale geospatial datasets, developing predictive models, and uncovering actionable insights to drive product innovation and improve user experiences.
Key Responsibilities:
- Data Analysis and Exploration: Analyze large-scale geospatial datasets, including map data, satellite imagery, and POI information, to identify patterns, trends, and anomalies and extract actionable insights to inform decision-making.
- Predictive Modeling: Develop and implement machine learning models and algorithms to predict and classify map features, identify points of interest, and optimize routing and navigation algorithms based on historical and real-time data.
- POI Data Enrichment: Enhance and enrich POI data by integrating external sources, such as business directories, social media, and user-generated content, and leveraging natural language processing (NLP) and entity recognition techniques to extract relevant attributes and information.
- Geospatial Analysis: Conduct geospatial analysis and spatial statistics to understand spatial relationships, proximity analysis, and spatial clustering of POI data and map features, and derive insights to improve location-based services and experiences.
- Data Visualization: Create visually compelling and informative data visualizations, maps, and dashboards to communicate findings, insights, and recommendations to stakeholders and decision-makers across the organization.
- Model Evaluation and Optimization: Evaluate the performance of predictive models and algorithms using appropriate metrics and techniques, and iterate on model design and parameters to improve accuracy, robustness, and scalability.
Qualifications:
- Master's or Ph.D. degree in computer science, statistics, geoinformatics, or a related field.
- 5 years of experience in data science, machine learning, or a similar role, with a focus on geospatial analysis, map data, or location-based services.
- Strong proficiency in programming languages such as Python or R, and experience with data science libraries and frameworks such as scikit-learn, TensorFlow, or PyTorch.
- Knowledge of geospatial data formats, GIS tools, and geospatial libraries such as GDAL, GeoPandas, or ArcGIS.
- Experience with machine learning techniques such as classification, regression, clustering, and spatial analysis, applied to geospatial data and mapping applications.
- Excellent problem-solving and analytical skills, with the ability to translate business requirements into technical solutions.
- Effective communication and collaboration skills, with the ability to work independently and as part of a multidisciplinary team.