Position: Senior Data Scientist
Company: TrendLine Global
Experience required: 5y
Category: Python
English level: upper
Published: 2020-01-01T00:00:00+02:00

•	Master’s degree (preferred) with emphasis on coursework of a quantitative nature (e.g., Statistics, Computer Science, Engineering, Mathematics, Physics, Data Science, Industrial/Organizational Psychology and Econometrics, etc.) and 5 years of experience working in a data analytical or computer programming function
•	Working knowledge of relational databases and standard SQL query methods
•	Proficiency in at least one general programming language such as  r, Python, Java or C/C++
•	Experience working with big data within a Hadoop environment
•	Experience working with R, SAS or other statistical packages
•	Advanced experience with statistical, econometric or data-mining tools and methods
•	Experience with supervised /unsupervised learning , linear regression, generalized linear regression, logistic regression , decision trees , random forests and ensemble methods 
•	Experience with high-performance Deep Learning frameworks such as TensorFlow, PyTorch, Theano
•	Strong oral, written, and interpersonal communication skills and an ability to work in a collaborative team setting
Responsibilities
•	Leverage R, Python, SQL, & Excel to dig into data to extract meaning from information. Interface closely with business teams to understand/define requirements, domain knowledge/models, and data needs.
•	Supports analytics projects and collaborates with cross-functional stakeholders to complete end-to-end analyses that includes business requirements, data gathering, analysis, scale-able solutions deliverables, including visualizations and presentations
•	Advanced-level knowledge and skills in data modeling, data structure, and the application of complex SQL queries with data from multiple sources, including a Big Data platform (e.g., Hadoop, AWS, Azure)
•	Conceptualize problems, apply appropriate theory, explore approaches, simulate, and implement AI /ML models 
•	Define data needs, evaluate data quality, and extract/transform data for analytic projects and research
•	Demonstrate a strong understanding of project scope, data extraction methodology, design of dependent and profile variables, logic and design of data cleaning, exploratory data analysis and statistical methods
•	Produce timely and error-free deliverables in an efficient manner, managing multiple tasks and varying project scopes
•	Explore and analyze source data and data flows, working with both structured and unstructured data; Manipulate high-volume, high-dimensional data from a variety of sources to identify value-generating patterns, anomalies, relationships, and trends
•	Utilize analytical applications like Python/R and recent Big Data technologies (Hadoop/Spark) to identify trends and relationships between different pieces of data, draw insightful conclusions and translate those analytical findings into next generation control algorithms