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Data Scientist (Fraud Detection, End to End Implementation) Inc

This is a Contract position in Toronto, ON posted November 17, 2020.

“The start date of this job is flexible and in order to find the best candidate the job may remain open much longer than the date mentioned on this posting…or you may be asked to start sooner than the expected start date if you are able to do so” URGENT: Please read the job description below.

If this interests you, please send a MS-Word version copy of your updated resume (ASAP) along with your salary expectations, first available date and a telephone contact number Please mention the job title above in the subject line The recruiter in charge of this role is Prakash If you do not think you are a match for this opportunity, but know someone who is, feel free to forward this email to them and you will be eligible for a referral bonus upon a successful hire.

One of our government clients is looking for a Data Scientist (Fraud Detection, End to End Implementation) Length: Contract term is 4 months with a possibility of extension Location: Toronto, ON (Position will start as remote and transition to onsite when safe to do so with the remainder of the team) Role: Ontario Government requires a resource to come in for the initial planning and implementation phase of the implementation of the fraud detection system.

This resource will assist with the selection of the solution and consider various options including vendor selections for off the shelf, building in house, etc.

Resource will identify what data is currently available and what Ontario Government needs.

They are looking for a resource with strong expertise in this area.

– Work with various business areas of the Ministry to support BIBA efforts in understanding and consolidating the Program/Business requirements for fraud/anomaly detection
– Draft a Business Case proposal for a project to address Fraud Detection requirements for the Ministry.

– Perform detailed analysis and determine the suitability of data science methodologies and tools as well as propose potential technical solutions
– Understand short from long term business objectives and come up with a roadmap
– Review various datasets available in terms of their suitability, and where gaps are found, suggest what other data might be needed to resolve specific problems.

– Propose technical solutions which may include developed in-house as well as off-the-shelf commercial products
– Develop a proof of concept to illustrate the technical solutions proposed
– Develop benchmarking tests for comparing various solutions proposed by external vendors
– Document the solutions proposed and organize workshops with business and IT teams and forge collaboration with stakeholders.

– Perform formal knowledge transfer for any solutions proposed Qualifications:
– Experience dealing with financial transactions in fraud detection techniques (ideally in a pay per claim model)
– Familiarity with fraud detection technics (rule-based, statistical/predictive models, AI) and software tools
– Strong business and data analysis skills
– Have solution designing experience in Fraud Detection platform setup including infrastructure and software
– Experience designing and building predictive models with both traditional data mining as well as modern machine learning/AI algorithms
– Familiarity with one or more AI toolkits (ex.TensorFlow, Torch, Keras)
– Expertise in open-source data science tools (preferably python)
– Experience in operationalizing predictive models
– Communication and presentation skills Must Haves:
– Strong and recent experience in end to end implementation and operations of a fraud detections
– Resource needs to understand what it takes to put a fraud detection solution into production.

– Background in fraud detection and data science a MUST
– Ideal background in open source fraud solutions, claims based fraud detection and commercial product experience
– Experience with Python a strong asset
– Resource needs to have technical knowledge of fraud detection systems (not just someone who has used them in the past) and also strong communication and interpersonal skills
– Experience with drafting business requirements or business use cases specifically related to fraud detection services
– Familiar with various AI toolkits (no specifics as one has not yet been selected) Nice to Haves:
– Financial data related to health-related services
– Processing financial claims (insurance industry)
– Home grown and/or commercial solutions for fraud detection Please note that this is the most up to date version of job description available at this time
– During Client Interview you will receive additional information
– variance may apply Please visit the TEAMRECRUITER website to review other CAREER OPPORTUNITIES