Position: Data Analyst
Company: Datrics
Experience required: 2y
English level: upper

**Job Description**

Perform full stack of data analysis process - conduct client meetings, collect, analyze and interpret historical data to help solve real-world business problems.

**Duties and Responsibilities**

- Analyze and interpret historical data to identify the specificity of relationships between various sources of information.
- Apply the machine learning methods to extract the hidden dependencies in the data and use them in the predictive pipeline.
- Evaluate sources of information to determine any limitations in terms of reliability or usability.
- Apply sampling techniques or utilize complete enumeration bases to determine and define groups to be analyzed
- Verify the hypothesis to discover mathematical bases for new or improved methods of obtaining and evaluating numerical data.
- Identify relationships and trends in data, as well as any factors that could affect the results of research.
- Design research pipelines to integrate valid scientific techniques and utilize information obtained from baselines or historical data to provide efficient analyses.
- Plan data collection methods for specific projects, and determine the types and sizes of sample groups to be used.
- Report and interpret results of statistical analyses, including information in the form of graphs, charts, and tables

**Qualifications**

Education:
- Masters or Bachelor's Degree in a quantitative field (Math, Statistics, Computer Science, Engineering, Data Science, Operations Research, etc.).

Specialized knowledge:
- Understanding the theoretical concepts of mathematical statistics and probability theory and their application to real-world problems
- Good knowledge in
		Applied statistics and statistical modeling
		Machine learning (classification, regression, anomaly detection,clustering):
				Linear Models
				Tree-based models
				Ensembles (bagging / boosting)
				Time Series analysis techniques (as a plus)
				Model evaluation
- Experience in applying machine learning techniques in solving real-world problems
- Experience in developing and implementation of statistical models
- Knowledge in Computer Science
- Ability to create and evaluate models in Python+NumPy/ SciPy/scikit-learn
- Understanding of software development lifecycles