Data Science Engineer

Data Science Engineer  

Duties & Responsibilities
• Subject matter expert of data analytics methodology, machine learning and deep learning.
• Understanding business objectives and developing data science solutions that help to achieve business goals, along with metrics to track their progress.
• Help product managers and business stakeholders understand the potential, limitations and results of data science.
• To conceptualize, prototype, design, develop and implement large scale data science solutions on premises or in the cloud.
• Utilizes internal and external data to generate features, derive insight, visualize pattern, and support impactful decision-making.
• Develop and implement advanced analytics use cases with machine learning, graph database, search engine, geographical information system and artificial intelligence to yield business value from data.
• Implement predictive modeling, multivariate analysis, research design, association rules, time series analysis, survival analysis, forecasting and optimization techniques to formulate and solve data analysis problems.
• Understanding the challenges of explainable machine learning and artificial intelligence, applying modern explainable methods to measure their fairness.
• Managing available resources such as hardware, software, data, and personnel so that milestones are met.

Job Requirements
• At least 3-5 years hands-on data science experience.
• Proficiency with SQL for querying and managing data in relational databases.
• Proficiency with Python-based libraries for data science such as scikit-learn, numpy, scipy, pandas, statsmodel, matplotlib, tensorflow or pytorch.
• Proficiency with building data pipeline and ETL process.
• Knowledge of other coding languages such as Shell Script and Java.
• Excellent team-oriented and interpersonal skills.
• Strong interest for creative problem solving.
• Good communication skills with the ability to clearly articulate findings and present solutions
• Experience with deep learning frameworks such as TensorFlow or PyTorch.
• Experience with natural language processing, image processing, graph analysis or GIS.
• Experience with real-time processing framework, such as Kafka.
• Experience with end-to-end Data Engineering and Data Science lifecycle.
• Experience with cloud-based environments such as AWS, GCP or Azure.