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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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