Gone are the days when data scientists would spend hours together analyzing data through SQL queries and working on those long Power BI/ MS Excel spreadsheets. WIth Gen AI being implemented across all industries, you need to sharpen your skills and accommodate yourself in areas that promote organizational growth despite the fast paced industry. Here’s a quick sneak peek on the current emerging job roles in data science.
Director of Gen AI Analytics
Oversees the data analytics and data warehousing teams, responsible for the research, development, and implementation of pertinent data systems.
The Director of AI is responsible for guiding the development and implementation of AI technologies throughout an organization. They establish strategic priorities, oversee teams of machine learning and data science experts, and ensure that AI aligns with long-term business objectives.
Key responsibilities encompass supervising research and experimentation, aligning AI applications with product and commercial strategies, directing model governance, and providing reports to executive stakeholders. Additionally, they collaborate with engineering, legal, and operations teams to manage risk and ensure compliance.
In scale-up environments, they may establish the AI function from scratch. In more established organizations, they lead large teams, manage partnerships, and influence how AI is incorporated into digital transformation initiatives. Setting the vision and roadmap for AI applications and platform capabilities.
- ● Managing teams across research, engineering, and MLOps functions
- ● Overseeing the deployment of AI models into production settings
- ● Aligning AI projects with product and business objectives
- ● Ensuring the ethical and compliant use of AI and data
- ● Assessing and selecting AI tools, vendors, and infrastructure
- ● Fostering innovation through partnerships, pilot projects, and emerging technologies
- ● Reporting AI results and ROI to executive leadership
- ● Leading recruitment and skill development across AI functions
- ● Collaborating with stakeholders in product, data, legal, and engineering.
Director of Gen AI Analytics
A principal AI data scientist designs, conducts, and documents research experiments across various fields and industries as part of a research team.
As a Principal Data Scientist, you hold the responsibility of spearheading data science projects and fostering innovation within the organization. Your proficiency in data analysis, machine learning, and statistical modeling allows you to tap into the potential of data and deliver actionable insights that promote business growth.
A key duty of a Principal Data Scientist is to develop and implement data management and strategy frameworks. This encompasses data collection, storage, integration, and ensuring the quality and integrity of data. By instituting strong data governance practices, you empower the organization to utilize data effectively while adhering to privacy and security regulations.
To create effective data management and strategy frameworks, a Principal Data Scientist performs comprehensive research and analysis to grasp the organization's data needs and requirements. This process includes assessing the current data infrastructure and systems, pinpointing gaps and areas for enhancement, and suggesting innovative solutions.
Data Computer Vision Engineer
Applies computer vision and machine learning research to address real-world challenges in real-time. A Data Computer Vision Engineer primarily focuses on creating and implementing systems that enable computers to comprehend and analyze visual data similarly to human vision.
A computer vision engineer is responsible for developing algorithms and models that empower machines to interpret and process visual information from images or videos. Their expertise is applied in areas such as facial recognition, object detection, medical imaging, robotics, and beyond. Some of their key responsibilities include:
- ● Developing and training models for object detection, tracking, and classification
- ● Preprocessing visual data and curating image/video datasets
- ● Utilizing frameworks like OpenCV, TensorFlow, or PyTorch
- ● Testing and assessing model performance against benchmarks
- ● Deploying models for real-time or batch inference
- ● Collaborating with software engineers to integrate models into applications
- ● Annotating datasets and managing data pipelines
- ● Monitoring performance in edge cases and refining algorithms
- ● Documenting methodologies, experiments, and model assumptions
- ● Keeping abreast of the latest advancements in computer vision research.
AI Algorithm Engineer
Aids clients in comprehending significant data trends and provides reports on these trends. An algorithm developer addresses computational challenges by researching, designing, and testing sequences. As an algorithm developer, you will probably engage with database management, operating systems, network security, and artificial intelligence to fulfill the objectives established by your employer. The terms "algorithm developer" and "algorithm engineer" are frequently used interchangeably. However, generally speaking, engineers implement higher-level engineering concepts, such as architecture, infrastructure, and system design, in software development. Conversely, developers concentrate on formulating a programming approach that aligns with the specific requirements of a project and are usually more hands-on. Below are some of the primary responsibilities:
- ● Creating algorithms tailored to the company's needs
- ● Designing software based on algorithms through coding and programming
- ● Testing algorithms for their effectiveness in AI tools, software, and machine learning
- ● Reporting on the algorithm's success in problem-solving and pattern recognition
- ● Maintaining and enhancing algorithms to accomplish designated tasks or resolve issues
- ● Collaborating with other tech teams to create algorithms that address specific needs
- ● Segmenting large data sets into smaller groups for better management and utilization
Computer Scientist
A computer scientist is a professional who applies specialized technical expertise, such as data utilization, software development, and trend analysis, to devise solutions for organizations. These specialists must grasp concepts including advanced mathematics, physics, and programming languages.
They may collaborate with computer engineers to design new computing systems or focus on specific fields like computer programming. A computer scientist investigates theoretical concepts, performs experiments, and leverages their knowledge to enhance existing technologies and develop more efficient applications. They can be employed by research institutions, universities, or private enterprises. Their responsibilities can differ based on their area of expertise, but common tasks include:
- ● Developing or modifying computer algorithms
- ● Creating new programming languages or writing code
- ● Assessing new computer systems or devices
- ● Formulating models or theories to address computing challenges
- ● Conducting research experiments to validate new theories
- ● Updating computer systems or software by designing new applications
- ● Enhancing computer systems and hardware to boost efficiency and speed
- ● Presenting theories and research findings to the scientific community through articles or presentations
- ● Educating others in the field and mentoring junior scientists
- ● Partnering with computer engineers and software developers to innovate new technologies
- ● Utilizing models and research to gather pertinent data
- ● Applying data to generate actionable insights and solutions
- ● Establishing databases to manage information for organizations
AI Data Statistician
Develops or employs various mathematical or statistical theories and methods to collect and interpret numerical data findings for specific projects. An AI Data Statistician employs artificial intelligence, machine learning, and sophisticated algorithms to streamline data cleaning, identify patterns, and create predictive models. By utilizing AI for functions such as data preprocessing, anomaly detection, and natural language processing, they significantly improve the speed and precision of statistical analysis compared to traditional methods, facilitating quick insights. Below are some primary duties of an AI data statistician.
- ● Data Preprocessing and Cleaning: AI streamlines the management of missing values, formatting issues, and inconsistent data, guaranteeing high-quality input for analysis.
- ● Pattern Recognition and Interpretation: Algorithms uncover intricate patterns, like customer behaviors or market trends, that are often too subtle for human detection.
- ● Predictive Modeling: AI models evaluate historical data to predict future outcomes, which is essential for risk assessment and strategic decision-making.
- ● Natural Language Processing (NLP): AI systems can interpret and summarize data through conversational prompts, enabling users to pose questions in straightforward English.
- ● Generative AI in Modeling: New AI tools can choose suitable statistical models and conduct tasks such as network meta-analyses.
AI Research Engineer
Leverages informed research outcomes to devise reliable solutions for existing problems. As an AI research engineer, you will collaborate with research scientists to transform research concepts into functional systems, developing the data, tools, and infrastructure that facilitate rapid iteration, reliable evaluation, and a seamless transition from prototype to production. Below are the primary roles and responsibilities of an AI research engineer in the field of data science.
- ● Create and manage datasets, training and evaluation pipelines, benchmarks, and internal tools.
- ● Execute models, conduct large-scale experiments, and assess for reliability, performance, and cost.
- ● Coordinate distributed training and distributed reinforcement learning using Ray, which includes scheduling, scaling, and recovery from failures.
- ● Enhance the research stack to be observable, reproducible, and user-friendly.
- ● Set up stringent automated benchmarks and regression tests for forecasting, anomaly detection, multi-modal analysis, agents, and code repair tasks.
- ● Work alongside Research Scientists, Product, and Engineering teams to embed advanced AI functionalities into Datadog’s product ecosystem and to refine prototypes into dependable services.
- ● Provide high-quality code, documentation, and open-source resources that empower both the community and internal teams to replicate, expand, and assess results.
Given that there are a lot of emerging job roles in AI powered data science, you can explore a bit more and find which role fits you best. At Eduinx, we will help you understand data science and generative AI through an industry relevant approach that keeps you up to date with the latest information. Our mentors are industry experts with over decades of experience in data science and AI. We will also offer placement assistance for you. Get in touch with us to know more about our PG diploma data science with generative AI course.
