Sr. Analytics Consultant - Jobs in Toronto

Job Description

Design and develop state-of-the-art data science and analytics solutions

1. Discuss key business objectives with the stakeholders and suggest data exploration strategies
2. Work with business executives to understand business constraints that may impact data science or machine learning models
3. Build Analytics frameworks to understand and predict customer behavior, sales forecasting, inventory, labor planning, pricing, etc.
4. Plan Data Collection Methods (E.g. Market Research, Internal Data, 3rd Party Data Vendors)amp; determine sample sizes.
5. Suggest new data sources and advise team on how to simulate market scenarios to enhance data assets.
6. Perform exploratory analysis and preliminary modeling to build hypotheses and document the solution approach
7. Explore various Data Science, Machine Learning, Artificial Intelligence techniques to find the best fit for the solution
8. Build end-to-end Analytics frameworks and models to understand and predict customer behavior, sales forecasting, inventory, labor planning, pricing, etc.
9. An example problem in the area of Marketing, would be to identify the optimal targeting mechanism for business prospects such that we know which prospects could be targeted through email, which ones through social media and which ones through Display or Paid Search, to customize the message and offer, thereby saving costs and increase conversion for the client

Contribute to Org building activities as described below

1. Build internal analytics modeling IP and accelerators for Tiger by providing insights into what business users are looking for and what they would pay for it
2. Support business development team in winning new data analytics projects by providing proof-of-concept solutions
3. Recruiting interviews to hire new employees to build world-class analytics talent
4. Work with clients and Tiger solutions team to come up with analytics platform and solutions such as automated insurance underwriting, market mix modeling, augmented text processing
5. Make prototypes and templates for frequently used modeling scenarios in open-source tools, such as R and Python
6. Write data science project proposals and make case study presentations to showcase company capabilities

Work on implementing data science and analytics solutions

1. Validate results of the model by benchmarking against historical data collected by the business users Qualify the models against legacy solutions to show error reduction benchmarks, such as mean square error, mean absolute percentage errors, etc.
2. Propose repeatable testing strategies for measuring and fine-tuning results produced by analytics models
3. Present the results of predictive models by converting it to usable business insights
4. Consult the client on adoption of big data frameworks and educate business stakeholder on traditional data systems vs big data systems by preparing a cost-benefit analysis
5. Extract, transform and organize the data into datasets using tools like SQL. Write complex SQL queries to retrieve data with low latency.
6. Ensure that the developed solution can be implemented in production by the client’s engineering team

Job Requirements:Education Qualification: Bachelor #39;s Degree*Salaries may increase annually based on performance. Additional bonuses may be issued at the company #39;s discretion and comparative market trends. If issued, the bonus may be in the form of Cash or ESOPS. COLA may be added as well at the company #39;s discretion.