Data Scientist

Key Skills

Data Analysis, Data Modeling, Data science techniques, Design, Effective communication skills, Machine learning techniques, Management, Planning and organization, PowerBI, PyTorch, SPARK Programming, TensorFlow

Job Description

We are seeking a talented and experienced Data Scientist to join our team at ATOS Data office. As a Data Scientist, you will be responsible for discovering valuable insights from our vast data sources and applying advanced analytics and machine learning techniques to drive data-informed decision-making across the organization

A data scientist is a professional who uses their expertise in data analysis, statistical modeling, machine learning, and programming to extract meaningful insights and knowledge from complex and large datasets. Data scientists apply their skills to solve business problems, drive decision-making processes, and uncover patterns or trends that can benefit organizations. 

Main Duties and Responsibilities

• Here are some key responsibilities and tasks typically associated with the role of a data scientist:
• Data Exploration and Preparation: Data scientists collect, clean, and preprocess large volumes of raw data from various sources. They perform exploratory data analysis to understand the characteristics and structure of the data. This involves identifying missing values, outliers, and data quality issues that may impact the analysis.
• Statistical Analysis and Modeling: Data scientists apply statistical techniques to analyze data and draw meaningful conclusions. They build descriptive and inferential models to uncover patterns, correlations, and trends within the data. This includes methods such as hypothesis testing, regression analysis, clustering, and classification.
• Machine Learning and Predictive Modeling: Data scientists develop and implement machine learning algorithms and models to make predictions or classifications based on data. They select appropriate algorithms, train models using labeled data, and evaluate model performance. This can involve techniques like decision trees, random forests, neural networks, and support vector machines.
• Data Visualization and Communication: Data scientists create visual representations of data to effectively communicate insights and findings to stakeholders. They use tools and libraries to generate charts, graphs, and interactive dashboards that convey complex information in a clear and understandable manner. They also prepare reports or presentations to share their results with non-technical audiences.
• Feature Engineering and Data Transformation: Data scientists engineer features by selecting or creating relevant variables from raw data that enhance the performance of machine learning models. They apply data transformation techniques such as scaling, normalization, and dimensionality reduction to improve model accuracy and efficiency.
• Collaboration and Cross-functional Communication: Data scientists work closely with various stakeholders, including business analysts, domain experts, and IT professionals. They collaborate to understand business requirements, define problem statements, and align data science solutions with organizational goals. Effective communication skills are crucial to translate technical concepts into actionable insights.
• Continuous Learning and Research: Data scientists stay updated with the latest advancements in data science, machine learning, and related fields. They continuously learn new techniques, algorithms, and tools to enhance their analytical capabilities. They explore academic research, attend conferences, and participate in online communities to expand their knowledge and stay ahead in the field.
• Overall, data scientists play a key role in leveraging data to extract valuable insights that drive business decision-making. Their combination of statistical expertise, programming skills, and domain knowledge allows them to uncover patterns, make predictions, and provide data-driven recommendations that contribute to the success of organizations across various industries.

Education

• Bachelor's degree in Computer Science, Statistics, Mathematics, or a related field. A Master's or Ph.D. in a relevant discipline is preferred.

Qualifications/Skills

• Proven experience as a Data Scientist or a similar role, with a strong track record of developing and implementing machine learning models.
• Proficiency in programming languages such as Python or R, and experience with libraries such as scikit-learn, TensorFlow, or PyTorch.
• Solid understanding of statistical analysis, hypothesis testing, and experimental design.
• Strong analytical thinking and problem-solving skills, with the ability to translate complex data into actionable insights.
• Experience with data visualization tools such as Tableau or Power BI.
• Familiarity with big data technologies such as Hadoop or Spark is a plus.
• Excellent communication and presentation skills, with the ability to effectively convey complex concepts to both technical and non-technical audiences.
• Strong attention to detail and a passion for accuracy in data analysis.
• Ability to work independently as well as in a team-oriented, collaborative environment.
• Strong organizational and time management skills, with the ability to handle multiple projects simultaneously.

Experience

• 5+ years of industry experience preferred