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• Data Scientists use machine learning and predictive analytics to get insights from massive
datasets to take impactful business decisions. They are responsible for collecting, analysing,
and interpreting data in decision-making. The data scientist role combines elements of
different technical jobs, including mathematicians, scientists, statisticians, and computer
programmer. This job profile requires proficiency in big data platforms such as Hadoop, Pig,
and Spark, along with fluency in programming languages such as SQL, Python, and Scala, and
a thorough understanding of descriptive and inferential statistics.
• Business Intelligence Developers analyse corporate and market trends to improve
profitability and efficiency. Their job profile includes creating and managing Business
Intelligence (BI) solutions, making technical questions, generating accurate search requests,
turning data into easy-to-understand business formats, working with business analysts and
colleagues to handle data, making visual tools to show data, and ensuring data is securely
stored and backed up. This post requires strong technical and analytical abilities, as well as
competence in data warehouse design and business intelligence technology.
• Robotics Engineers construct and manage AI-powered robots and mechanical devices that
can perform tasks based on human commands. Their tasks involve researching different areas
within robotics, such as nanotechnology, creating processes and prototypes for machine
construction, and conducting tests on robotic systems. Programming proficiency, as well as
knowledge of fields like mechanical engineering and electrical engineering are required for
success in this field.
• AI Engineers develop and maintain AI applications using cutting-edge technologies. Their
task involves developing diverse AI applications, ranging from contextual advertising, utilising
sentiment analysis to visual identification or perception and language translation. Proficiency
in software engineering, programming languages, and statistical analysis, along with strong
knowledge of computer science, engineering, or related subjects is required in this field.
• Natural Language Processing (NLP) Engineers focus on voice assistants, speech recognition,
and document processing. Their task involves reviewing and refining data science prototypes,
crafting NLP applications, choosing suitable annotated datasets for Supervised Learning
approaches, employing efficient text representations to convert natural language into
valuable features, identifying and integrating appropriate algorithms and tools for NLP
objectives. A person with speciality in computational linguistics, or a combination of computer
science, mathematics, and statistics, are usually required in this field.
• Computer Vision Engineers create algorithms and systems to analyse and interpret visual
information in photos and movies. Computer vision engineers utilise research in computer
vision and collaborate with object-oriented software to manage the processing and analysis
of extensive datasets, aiming to facilitate predictive decision-making automation through
visual cues like images and videos. Their expertise rests in developing software solutions that
can analyse and process visual data, with knowledge of image processing techniques as well
as programming languages like Python and C++.
• AI Ethicists oversee the ethical aspects of AI technology development and implementation,
assuring responsible and ethical use. Their main duty includes creating ethical guidelines,
auditing AI systems for biases, collaborating with stakeholders, staying updated on AI
advancements, engaging with the public, advising policymakers, assessing ethical risks, and
partnering across disciplines. They advise on ethical frameworks, rules, and practices to
promote fairness, transparency, and accountability in AI systems, which require a background
in ethics, philosophy, or law, as well as competence in AI technology.
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