TCS Pace Port Toronto- ON-387

Project type: Research
Desired discipline(s): Engineering - computer / electrical, Engineering, Computer science, Mathematical Sciences
Company: Tata Consultancy Services
Project Length: 6 months to 1 year
Preferred start date: 12/01/2020
Language requirement: English
Location(s): Toronto, ON, Canada
No. of positions: 1
Desired education level: CollegeUndergraduate/BachelorMaster'sPhDPostdoctoral fellow
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About the company: 

We ensure the highest levels of certainty and satisfaction through a deep-set commitment to our clients, comprehensive industry expertise and a global network of innovation and delivery centers.

Our mission is to help customers achieve their business objectives by providing innovative, best-in-class consulting, IT solutions and services and to make it a joy for all stakeholders to work with us.

We function as a full stakeholder to business, offering a consulting-led approach with an integrated portfolio of technology led solutions that encompass the entire Enterprise value chain. Our Customer-centric Engagement Model defines how we do engage with you, offering specialized services and solutions that meet the distinct needs of your business.

We build bespoke teams around your domain and technology requirements drawn from our talent pool of over 453,540 global professionals including 36.4% women from 147 nationalities. Our domain expertise has been built upon decades of experience working across industries and this knowledge underpins our suite of solutions.

Describe the project.: 

TCS PaceTM is an integrated brand representing "Agile" innovation across TCS with accelerated business outcomes. TCS Paceport is a prominent channel of bringing the Pace (TM) philosophy to life and inspiring customers and partners through a combination of storytelling, co-creation and co-innovation

Pace Port Toronto is a co-innovation and research hub designed to be a critical enabler for Canada’s banking, financial services and insurance sectors.  
Located in the vibrant heart of Toronto’s downtown core and North America’s fastest growing tech market,
By leveraging the power of a global collaborative ecosystem of start-ups, technology partners, academia plus TCS research and innovation, we are helping businesses across all industries to emerge from the global pandemic with a keen eye on driving growth and digital transformation.

TCS mission is to help customers achieve their business objectives by providing innovative, best-in-class consulting, IT solutions and services and to make it a joy for all stakeholders to work with us.

The researcher will be required to do the following:

  • Design, build and evaluate machine learning models for predictive learning.
  • Use statistical techniques, machine learning, and data mining to build scalable and innovative solutions.
  • Develop automated processes for large scale data analysis model validation, development, and implementation.
  • Research, implement and deploy new machine learning and statistical approaches for profitable business decision making.


Required expertise/skills: 

Machine Learning Intern with following skills and assets:

  • Computer Science Fundamentals and Programming 
  • Probability and Statistics
  • Data Modelling and Evaluation
  • Applying ML Algorithms and Libraries
  • Software Engineering and System Design
  • Knowledge of data structures like arrays, stack, queue, graphs, and trees.
  • Theoretical, joint and conditional probability
  • For making predictions – Classification, regression, anomaly detection, etc.
  • Know-how of machine learning models – SVM, KNN, decision trees, neural networks, etc.
  • Software engineering best practices ( requirement analysis, design, version control, modularity, testing, documentation, and deployment)
  • Computer architecture basics – Distributed Processing, Cache, Memory, and other basic OS concepts.
  • Techniques derived from probability –  Bayes theorem, hidden Markov models, Markov decision processes, and more. 
  • For identifying patterns – Correlations, eigenvectors, clusters, etc.
  • Knowledge of learning procedures – linear regression, boosting, bagging, gradient descent, and other model-specific learning procedures.
  • Adoption of agile practices for machine learning.
  • Computability and time and space complexity of algorithms –  P vs NP,  NP-Complete vs NP-hard,  big-o-notation, and more.
  • Measures of centre: mean, mode, median, and mid-range. Measures of position: percentile, quartile, and Z-scores measures of variability: Standard deviation, range, and variance.
  • Evaluation strategies – sequential vs randomised cross-validation, training vs testing split.
  • Knowing the usage of commonly used machine learning libraries – SciKit Learn, PyTorch, Theano, TensorFlow, H2O, Spark MLlib, etc.