Unleash your ambition as a Senior Data Scientist! We are looking for a professional who not only has impressive experience in data science but is also ready to take on a driver role, contributing to our Data Competency Center within Sigma Software. The role will involve Solution assessment, pre-sales activities under supervision, architecture design composition, and more.

PROJECT

We are a team of 160+ professionals. We are very different, but a few things make us a true team: a genuine passion for our work, friendliness, and inexhaustible optimism, no matter what.

We use Agile with technical excellence in place and Kanban approaches to do great work and make customers happy. Our goal is to offer our clients the best expertise in different domains to bring value to their business and become the best tech partner.

Job Description

- Work closely with the client (PO) as well as other Team Leads to clarify the tech requirements and expectations

- Translate complex business problems into actionable data-driven questions or hypotheses. This will involve working with stakeholders to understand the underlying issues and defining the specific questions that need to be answered through data analysis

- Collect data from various sources, identify and address data quality issues, and convert the data into a format suitable for analysis and modeling. This will involve using data wrangling techniques, data cleaning tools, and data quality checks

- Apply statistical methods, data visualization techniques, and machine learning algorithms to uncover hidden patterns, trends, and anomalies within the data. These insights will inform the development of effective models and solutions

- Select and apply appropriate machine learning or statistical models to address specific business problems. This involves understanding the problem at hand, choosing the right algorithms, training the models on the prepared data, and evaluating their performance

- Work with engineers to integrate trained models into production environments, ensuring that they can be used to make real-time predictions or decisions. This involves deploying the models, monitoring their performance, and maintaining them over time

- Effectively communicate complex data-driven insights and recommendations to stakeholders in a clear, concise, and actionable manner. This involves using storytelling techniques, visualizations, and presentations to effectively convey the findings and their implications for business decisions

- Continuously research and stay up-to-date with the latest advancements in data science, including new algorithms, techniques, and tools, and explore emerging technologies and methodologies. This involves attending conferences, reading research papers, and experimenting with new approaches

Suggest and contribute to training and improvement plans regarding analytical data engineering skills, standards, and processes

PERSONAL PROFILE

- Analytical and Problem-Solving Skills:

- Demonstrated problem-solving and analytical thinking skills, with a proven track record of applying these skills to real-world challenges to identify problems, gather relevant data, and develop creative solutions

- Continuous learning mindset, ensuring you stay up-to-date with the latest advancements in deep learning and adapt skills accordingly

- Actively participate in the evaluation of new tools for analytical data engineering or data science

Qualifications

- Expertise in machine learning algorithms, including linear regression, clustering, classification, and recommendation systems

Proven ability to create clear, concise, and compelling visualizations using tools like ggplot2, matplotlib, or plotly, Power BI, Tableau, and Qlik

- Conceptual understanding of data analysis fundamentals, encompassing ETL, data warehousing, and unstructured data

A comprehensive grasp of deep learning fundamentals, including activation functions, backpropagation, CNNs, Transformers, transfer learning, and generative models Expertise in evaluating and selecting the most appropriate deep learning model for a given task, including assessing model performance metrics, identifying potential biases, and comparing different model architectures