Technical Manager-Data Scientist-India

We are a digitally native company that helps organizations reinvent themselves and unleash their potential. We are the place where innovation, design and engineering meet scale. Globant is 20 years old, NYSE listed public organization with more than 26500 employees worldwide working out of 25 countries globally.

www.globant.com

Our expertise in Data & AI allows our studio to create a wide variety of end to end solutions for industries including finance, travel, media & entertainment, retail, health, among others. We democratize data and foster organizational changes towards a data-driven culture.

Experience: 12- 18 Years

Job Location : Pune/Bangalore/ Ahmedabad/Indore/ Chennai

WHO ARE WE LOOKING FOR?

Globant is looking for a Tech Manager(TM) Data Scientist who has a very strong background in statistics and has hands-on experience with advanced data analysis and machine learning techniques. Candidates are expected to have the experience of working with clients to understand and solve problems or

improve processes using AI and ML. As a data scientist SME, the candidate should be able to provide, execute and deliver end-to-end solutions within the context of the digital space. Strong experience on Azure ML, Azure Cognitive Services, NLP, LUIS, Azure Search is preferred. The candidate should assume the responsibility of driving a team through the complete life cycle of a software project. Responsible for the overall estimation, execution, and success of a complex technical project. A go-getter who can learn and adapt quickly as well as coach/mentor the team on different technological advancements related to Data Science.

YOU WILL GET THE CHANCE TO:

As a data scientist TM, you will be domain agnostic and hence you'll be able to work in different domains like finance, pharmaceutical, media and entertainment, manufacturing, hospitality and so on.

You will get the exposure to the following:

❖ Lead multiple teams of Data Scientists who’ll be working on different projects and will be dealing with problems like anomaly detection, time series forecasting, building bots, classification, regression, clustering, recommendations etc.

❖ Coach project team members in good design and coding practices, Institutionalize good practices. You’ll be receiving and analyzing information on ongoing projects and sharing knowledge through several teams in account start-ups.

❖ Attend conferences and research of different technologies.

❖ Execute technical pre-sales for different problem statements of the customer/business.

❖ Lead one more practice on Data Science such as NLP, Image Processing, Pattern Recognition etc.

❖ Being an active member of the Technical hiring panel to hire for the best Data Science talent.

❖ Design and facilitate different training on Data Science to increase the overall knowledge quotient of the Artificial Intelligence Studio.

❖ Design and facilitate multiple POCs/POVs to the existing customers or prospective leads.

❖ Preserving the knowledge through these researches and innovation and utilizing it to enhance the overall capability of the Artificial Intelligence Studio.

❖ Proactively interacts with the client and takes important technical decisions regarding design and architecture.

❖ Agree scope, priorities and deadlines with the project managers.

❖ Describe problems and provide solutions to the appropriate staff in a clear and accurate manner.

❖ Assure the overall technical quality of the solution.

❖ Estimate the time of development tasks and perform difficult/critical coding tasks.

❖ Defining metrics and setting objectives in multiple complex projects.

WHAT WILL HELP YOU SUCCEED?

A strong base in statistics along with deep learning is mandatory. Strong experience on advanced data analysis and machine learning techniques. You should be well versed with techniques like error analysis which can help you improve your models developed. Knowledge of the model life cycle to continuously improve the model after production deployment is essential. Always staying relevant with the latest developments in the field of data science and advocating it to the other Data Scientists within the organization. An expert professional with a lot of zeal to learn and explore new methodologies. It is essential that you

work in a collaborative environment and come up with innovative ideas to continuously improve the solutions / models. Candidates must be willing to explore and research newer areas / technology / algorithms and look to continuously improve the models.

Technical Skills

❖ Linear Algebra, Probability and Statistics

❖ Strong in Python or R

❖ Exploratory Data Analysis and Data Visualization. This includes using the statistical and visualization libraries like numpy, pandas scipy, matplotlib or software like PowerBI, Tableau. It is also good to have a exposure to statistical analysis packages like SAS and SPSS

❖ Must have expertise in Hypothesis testing or A/B Testing

❖ Supervised and Unsupervised ML using statistical and deep learning techniques. Candidates should be familiar with parametric and non-parametric machine learning methods.

❖ They should be able to define a sensible evaluation metric for the machine learning models.

❖ Exposure to automated machine learning is an added advantage.

❖ Exposure to advanced machine learning algorithms will be an added advantage. This can include reinforcement learning techniques, genetic algorithms, semi-supervised learning methods etc.

❖ Candidates should be able to extract data from multiple data sources. This includes, and is not limited to SQL, NoSQL and Graph databases. Familiarity with SQL and No-SQL query languages is essential.

❖ Data structures and algorithms including space and time complexity requirements.

❖ Candidates are expected to have an expertise in statistical methods including ANOVA multivariate regression, exploratory and confirmatory factor analysis, multidimensional scaling and cluster analysis.

❖ Exposure to model deployment lifecycle. This includes exporting the model, using REST services to expose the model, defining KPI to continuously monitor model performance, model re-training and transfer learning and basic knowledge of MLOps

❖ Experience on at least one of the cloud platforms viz. Azure, GCP or AWS with services like serverless computing, deploying models using docker and kubernetes.

❖ Exposure to Big data tools like Spark, Hadoop, Hive.

❖ NLP / NLU

• Must have experience on common NLP use cases like Extractive and Abstractive summary generation, sentiment analysis, topic classification, translation
  
  

etc.

• Should have worked on Transformer Models like BERT, to build Intent classification, Named Entity Recognition (NER) and Q&A systems, for both training
  
  

and inference.

• Exposure to frequently used models in NLP like Attention based RNN, LSTM, BERT and GTP and generative AI techniques
• Should be able to use open source services and build custom models using transfer learning for use cases like speech to text and text to speech, generate
  
  

transcripts of video and audio files, etc.

❖ Computer Vision

• Must have experience on common computer vision use cases like face recognition, object detection, image classification, pose estimation, medical image
  
  

analysis, edge detection etc

• Should have experience on image preprocessing concepts like image filtering, noise reduction, image masking, image segmentation etc
• Should be well versed with commonly used models like CNN, GAN and libraries like opencv while working on image and video analysis.
  
  

❖ AI

• Should be able to solve problems using classical search algorithms, constraint satisfaction problems and adversarial search algorithms
• Should have worked on problems which involve planning and reasoning using logical agents
• Candidates should be able to solve problems when there is uncertainty by using probabilistic reasoning to make simple and complex decisions.
  
  

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