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DATA
SCIENTIST

A data scientist utilizes statistical analysis, machine learning techniques, and programming skills to extract insights from large and complex datasets. They apply data-driven approaches to solve problems, develop predictive models, and make data-driven decisions. Their expertise lies in uncovering patterns and trends to drive business outcomes and optimize processes.

LOCATION

Sri Lanka

EMPLOYMENT TYPE

Permanent

What You’ll Do

  • Data Analysis: Analyze sensor data, system logs, and other relevant data generated by robotic systems to identify patterns, anomalies, and insights that can improve the performance, efficiency, and safety of the robots.

  • Machine Learning and AI: Develop and implement machine learning algorithms and AI models to enable robots to learn from data, make intelligent decisions, and adapt to changing environments. This can involve tasks such as object recognition, path planning, and autonomous decision-making.

  • Predictive Maintenance: Utilize data analytics techniques to predict and prevent equipment failures and optimize maintenance schedules for robotic systems, ensuring maximum uptime and reducing downtime.

  • Optimization: Use data-driven approaches to optimize various aspects of robotic systems, such as energy consumption, movement efficiency, and resource allocation, to enhance overall performance and productivity.

  • Data Integration: Collaborate with engineers and software developers to integrate data collection and analysis capabilities into the robotic systems, ensuring seamless data flow and real-time insights.

  • Performance Evaluation: Develop metrics and methodologies to evaluate the performance of robotic systems, assess their efficiency, accuracy, and reliability, and propose improvements based on data analysis.

  • Research and Innovation: Stay updated with the latest advancements in robotics and AI, explore new techniques and technologies, and contribute to research projects to drive innovation within the company.

Who You are

  • Education and Qualifications: The company will expect the candidate to have a relevant degree, such as a Bachelor's or Master's degree in a field like data science, computer science, statistics, or a related discipline. They may also specify preferred qualifications or certifications, such as a Ph.D. or industry certifications in data science or machine learning.

  • Technical Skills: Companies will look for proficiency in programming languages commonly used in data science, such as Python or R. They may also require knowledge of data manipulation and analysis tools, like SQL, and experience with machine learning frameworks and libraries, such as TensorFlow or PyTorch. Expertise in statistical analysis, data visualization, and big data technologies can also be valuable.

  • Machine Learning and Data Analysis: Companies will expect a strong understanding of machine learning algorithms, statistical modeling, and data analysis techniques. Experience with exploratory data analysis, feature selection, model validation, and optimization is often required. Demonstrated expertise in applying machine learning to real-world problems, such as predictive modeling or anomaly detection, is highly desirable.

  • Programming and Software Development: Proficiency in programming and software development practices is crucial. Companies may require experience with version control systems (e.g., Git), software engineering principles, and the ability to develop clean, efficient, and maintainable code. Knowledge of software development lifecycle and agile methodologies may also be requested.

  • Domain Knowledge: Depending on the industry or engineering specialization, companies may seek candidates with domain-specific knowledge. This can include familiarity with robotics, AI, Internet of Things (IoT), autonomous systems, or other relevant engineering fields.

  • Data Visualization and Communication: Effective communication and data visualization skills are important for conveying insights to stakeholders. Companies may ask for experience in presenting complex data findings in a clear and understandable manner, both orally and visually.

  • Problem-Solving and Analytical Abilities: Strong problem-solving skills, analytical thinking, and the ability to work on complex data challenges are highly valued. Companies may require candidates to showcase their ability to approach problems creatively, think critically, and provide data-driven solutions.

  • Collaboration and Teamwork: Data scientists often work in interdisciplinary teams. Companies may look for candidates who can collaborate effectively with engineers, domain experts, and other stakeholders, demonstrate teamwork skills, and communicate effectively in a team environment.

  • Continuous Learning and Adaptability: Given the fast-paced nature of the field, companies may seek candidates who demonstrate a passion for continuous learning, staying updated with the latest advancements in data science and AI, and adapting to new tools and technologies.

  • Research and Innovation: Companies may value candidates who have a track record of research contributions, publications, or patents in the field of data science, demonstrating their ability to innovate and push the boundaries of the field.

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