Data Scientist

Responsibilities:

Collect, preprocess, and analyze large datasets to extract valuable insights and patterns.

Develop and implement machine learning models and algorithms to address business challenges and opportunities.

Collaborate with cross-functional teams to identify key business problems and propose data-driven solutions.

Design and conduct experiments to validate hypotheses and improve model performance.

Visualize and communicate findings to stakeholders using data visualization techniques
and storytelling.

Stay updated on the latest trends and advancements in data science, machine learning, and
artificial intelligence.

Continuously evaluate and improve existing models and algorithms based on feedback and
new data.

Work closely with data engineers to ensure data quality, consistency, and accessibility for
analysis.

Requirements:

Student Master’s/ Ph.D. or graduate in Computer Science, Statistics, Mathematics, or a
related field.

Experience as a data scientist, with a first portfolio of projects.

Proficiency in programming languages such as Python or R, and familiarity with data
manipulation and analysis libraries (e.g., Pandas, NumPy, SciPy).

Solid understanding of machine learning algorithms and techniques, including supervised
and unsupervised learning, regression, classification, clustering, and deep learning.

Experience with data visualization tools and techniques (e.g., Matplotlib, Seaborn, Tableau).

Strong analytical and problem-solving skills, with the ability to think critically and creatively
about complex problems.

Excellent communication and collaboration skills, with the ability to explain technical
concepts to non-technical stakeholders.

Ability to work independently in a fast-paced environment.

Familiarity with big data technologies and tools (e.g., Hadoop, Spark) is a plus.

Knowledge of cloud platforms such as AWS, Azure, or Google Cloud Platform is desirable.

Qualifications:

Demonstrated success in applying data science techniques to real-world problems and
delivering business value.

Experience with end-to-end data science projects, from data collection and preprocessing
to model deployment and monitoring.

Strong understanding of statistical concepts and methodologies.

Experience with SQL and database systems for data retrieval and manipulation.

Familiarity with software development best practices, version control systems (e.g., Git),
and agile methodologies.

A passion for continuous learning and self-improvement, with a commitment to staying
updated on industry trends and advancements.

Ability to adapt quickly to new technologies and tools, and a willingness to experiment with
new approaches to solve problems.