With a large volume of data being generated across the world, enterprises are looking at every minute aspect of these data to provide a nuanced experience for scores of its users.
With high demand and competition driving the market presently, the role of data scientist and data analyst are also fast evolving. While data janitor was the funky new designation in early 2016, the role of machine personality developer will be the hottest job in the coming years. We look at some of these funky designations and what it means to do the job that they do.
Head of Machine Personality Developer: With virtual assistants and chatbots getting smarter terms of intelligence and emotions, the role of a Machine Personality developer will be to give a unique character to the product by tapping into the personality algorithm.
Considered to be a futuristic job, a Machine Personality developer is required to inject the “voice of the customer” into the machine personality by capturing the desired customer preferences and aversions at the point of delivery. He/she would also have to tune the personality algorithm with inputs and outputs to the machine interface to fine-tune the offering post-implementation.
The Data Architect: This job requires the specific person to work with the application and software application team to recommend database structures based on the data storage and retrieval needs within each department. They will also have to monitor the health of the database, fix issues if any and compiles reports on how the organisation is using their data and changes in the pattern of usage. Knowledge of C and PHP languages, ability to work in distributed systems, proficiency with Oracle are of the skills required for this job.
Database Caretaker: The primary job of a data science caretaker will be to oversee that the database is available to all the relevant users. He/she should also ensure the availability and safety of the database. Other responsibility of the person also includes defining database physical structure and functional capabilities, database security, data back-up, and recovery specifications. Database Performance Tuning, Database Security, Promoting Process Improvement, Problem Solving etc are some of the skills required for the job.
Data Science Quality Analyst: This work includes checking the quality of the training dataset, preparing datasets for testing, running statistics on human-labelled datasets, evaluating precision and recall on the resulting ML model, reporting on unexpected patterns in outputs, and implementing necessary tools to automate repetitive parts of the work. Bachelor’s Degree in Computer Science, Statistics, or other quantitative fields, experience in software testing with data quality or DS/ML focus, understanding of statistics, exposure to Data Science / Machine Learning techniques and coding proficiency in Python, are some of the skills required for the job
Sustaining Engineer: This engineer will work directly with customers to troubleshoot issues, problems and engage closely with internal product engineering organization to come up with solutions, workarounds, product feature/bug requests and make sure the customer issues are resolved in a timely manner abiding by different SAL rules. They will also proactively provide recommendations on ways to improve productivity and customer satisfaction levels. A background in engineering and customer-facing technical roles is required. We are looking for mindset over skill set, and fast learners with a strong sense of customer empathy.
Risk Data Scientist: The primary task of a person in this role will be to research internal and external data to identify fraudulent behaviours such as account takeover, credit card fraud, merchant risk, collusion schemes, syndicated fraud attacks as well as money laundering schemes. He/she will have to use statistics and anomaly detection to recognize fraudulent and abusive behavioural patterns and derive actions such as risk rule writing.