Role Proficiency:

Independently interprets data and analyses results using statistical techniques

Outcomes:

      Independently Mine and acquire data from primary and secondary sources and reorganize the data in a format that can be easily read by either a machine or a person; generating insights and helping clients make better decisions.       Develop reports and analysis that effectively communicate trends patterns and predictions using relevant data.       Utilizes historical data sets and planned changes to business models and forecast business trends      Working alongside teams within the business or the management team to establish business needs.       Creates visualizations including dashboards flowcharts and graphs to relay business concepts through visuals to colleagues and other relevant stakeholders. Set FAST goals

Measures of Outcomes:

      Schedule adherence to tasks       Quality – Errors in data interpretation and Modelling       Number of business processes changed due to vital analysis.       Number of insights generated for business decisions       Number of stakeholder appreciations/escalations       Number of customer appreciations No: of mandatory trainings completed

Outputs Expected:

Data Mining:

Acquiring data from various sources

Reorganizing/Filtering data:

Consider only relevant data from the mined data and convert it into a format which is consistent and analysable.

Analysis:

Use statistical methods to analyse data and generate useful results.

Create Data Models:

Use data to create models that depict trends in the customer base and the consumer population as a whole

Create Reports:

Create reports depicting the trends and behaviours from the analysed data

Document:

Create documentation for own work as well as perform peer review of documentation of others' work

Manage knowledge:

Consume and contribute to project related documents
share point
libraries and client universities

Status Reporting:

Report status of tasks assigned Comply with project related reporting standards and process

Code:

Create efficient and reusable code. Follows coding best practices.

Code Versioning:

Organize and manage the changes and revisions to code. Use a version control tool like git
bitbucket
etc.

Quality:

Provide quality assurance of imported data
working with quality assurance analyst if necessary.

Performance Management:

Set FAST Goals and seek feedback from supervisor

Skill Examples:

      Analytical Skills: Ability to work with large amounts of data: facts figures and number crunching.       Communication Skills: Ability to present findings or translate the data into an understandable document       Critical Thinking: Ability to look at the numbers trends and data; coming up with new conclusions based on the findings.       Attention to Detail: Making sure to be vigilant in the analysis to come with accurate conclusions.       Quantitative skills - knowledge of statistical methods and data analysis software       Presentation Skills - reports and oral presentations to senior colleagues       Mathematical skills to estimate numerical data.       Work in a team environment Proactively ask for and offer help

Knowledge Examples:

Knowledge Examples

      Proficient in mathematics and calculations.       Spreadsheet tools such as Microsoft Excel or Google Sheets       Advanced knowledge of Tableau or PowerBI       SQL       Python       DBMS       Operating Systems and software platforms       Knowledge about customer domain and also sub domain where problem is solved Code version control e.g. git bitbucket etc

Additional Comments:

Who we are? You will be part of the ML R&D team which works on some really cool problems and (sometimes not-so-cool :-) problems). We apply cutting edge ML to solve hard problems like Document Understanding (or Document Al). We have a solution in production which is on par with the industry players in multiple facets. We reason things from the 1 st principles, or we build on top of existing things as the problem dictates. We as a team push the boundary of ML and constantly work on techniques to solve problems with no or little training data. We are a very flat org; everyone is technically sound and very collaborative. Your typical day would involve creating datasets from the scratch or run multiple iterations of feature engineering or come up with a great representation learning technique or conceptualize a nifty transfer learning solution, fit a model to the data and package the model to serve in batch or in online fashion. Who we are looking for? • We are flexible and are looking for the top talent ideally with 3-5 years industry experience or 1-2 years academic experience. • Programming Experience: Ninja Programmer in one of the following Python/ R. • Applied ML Experience: o Problem framing: ■ Strong problem framing skills: Say, when to go with Supervised or self-supervised or RL setting. o Data wrangling skills: ■ Experience in techniques like Weak/Distant Supervision and Pseudo labelling) ■ Strong EDA, data preparation and labelling skills ■ Strong data augmentation skills o From the scratch learning: ■ Strong experience in end to end modelling in (ML vs DL vs RL), ■ Experience in Single models vs Ensembles vs Mixture of experts. ■ Mathematical understanding of some Mathematical Induction, Tree Induction, DL and other optimization algorithms like SGD. o Transfer Learning ■ Experience in N-shot learning (or its variants) ■ Fine tuning skills UST Global Ltd 1 SmartOps Strategic R&D o ML/DL Verticals: Proven research or industry experience in one of the areas like Time series modelling, Vision, NLP, RL. • A GitHub portfolio with original ML repos. • A Kaggle portfolio with decent leader board positions • Papers: Original 1 st author papers in reputed ML journals or conferences. • Patents: Al or Automation specific patent is a good to have • Experience with ML/DL libraries TensorFlow or PyTorch • MLOps: Experience in running machine learning experiments with any one of the above machine learning libraries. Good to have is any one of the following: Kubeflow, Mlflow or Airflow or SparkML • Deploying machine learning solutions into production. Model Serving TFServe, Seldon, Custom serving. Interactive, batching and streamed serving. • Optimizing solutions for performance and scalability. • Data engineering, i.e. ensuring a good data flow between database and backend systems. • Implementing custom machine learning code (like custom implementation of existing algorithms like SGD) when required • Coming up with our own DNN architectures when required • Good to have: Computer science or IT background • Good to have: Exposure to statistics and probability. • Good to have: Experience in running dockerized code, we are a Kubernetes shop