| Описание: | Responsibilities:- Analyze time series data collected from various sensors in the oil and gas industry.
- Develop and implement data preprocessing and cleaning strategies to ensure data quality.
- Perform feature extraction and engineering to improve model performance.
- Apply machine learning and statistical models to identify patterns, anomalies, and trends in the data.
- Develop predictive models and algorithms to forecast future trends and events.
- Collaborate with cross-functional teams, including engineers, domain experts, and other data scientists, to develop and deploy data-driven solutions.
- Interpret and communicate findings to stakeholders through clear and concise reports and visualizations.
- Continuously evaluate and refine models and methodologies to improve accuracy and efficiency.
- Stay up-to-date with the latest advancements in data science, machine learning, and the oil and gas industry.
- Analyzing large amounts of information to find patterns and solutions. Carrying out preprocessing cleansing and validating the integrity of structured and unstructured data to be used for analysis across different projects.
- Presenting results in a clear manner
Requirements:- Bachelor’s degree in computer science, IT, Mathematics/Physics or similar field; a master’s is a plus
- 3 years’ experience as a data engineer/scientist or in a similar role. Delivered several big projects with hands on experience.
- Excellent knowledge of statistical programming languages like Python (OOP must), and hands on experience database query languages like SQL. Understanding of data structures, data modeling, ML algorithms and software architecture. ML frameworks (like TensorFlow or PyTorch) and libraries (like scikit-learn).
- Proficiency in handling imperfections in data is an important aspect of a data scientist job description.
- Good applied statistical skills, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators, etc. Proficiency in statistics is essential for data-driven companies.
- Hands on Experience with of Machine Learning techniques, including decision tree learning, clustering, artificial neural networks, NLP etc. on several projects. Ability to create pure or hybrid custom deep learning or computer vision architectures.
- Foundation in mathematics and statistics, including probability theory, statistical inference, linear algebra, and calculus.
- Experience with statistical software and techniques for hypothesis testing, regression analysis, and time series forecasting
- Strong knowledge of time series analysis techniques and tools.
- Experience with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.
- Familiarity with data visualization tools such as Matplotlib, Seaborn, or Tableau.
- Experience with big data technologies such as Hadoop, Spark, or similar is a plus.
- Knowledge of SQL and experience with databases.
- Proficiency in common machine learning algorithms such as regression, classification, clustering, and anomaly detection.
- Experience with deep learning techniques for time series forecasting and anomaly detection.
- Experience with Gitlab, and Linux environments
- Can independently assemble a prototype from business requirements and data collection to directly training the model and creating a prototype, API for subsequent rollout
- Strong problem-solving skills and the ability to work with complex datasets.
- Ability to interpret and communicate technical information to non-technical stakeholders
Working conditions:- Relocation package (residence permit for an employee and his family members, relocation payment for the employee and his family members, payment of apartment rent for the first month)
- Medical Insurance
- Child Support
- Wellness Support
- Training and Development
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