Lead Data Scientist

Royal Bank of Canada

Job title:

Lead Data Scientist

Company

Royal Bank of Canada

Job description

Job SummaryJob DescriptionWhat is the Opportunity?The Personal Banking & Commercial Banking Data Strategy team is looking for a passionate and innovative Lead Data Scientist within our advanced marketing pillar. As a Data Scientist you will problem solve, analyze, design, implement and monitor machine learning models and applications using state of the art tools and algorithms. We are looking to maintain our position as an industry leader in advanced digital marketing techniques (Multi-touch Attribution, Media Mix Modeling and Testing Strategies) and continue to drive strong client acquisition results. To achieve these goals, we leverage diverse data sources and a suite of domain specific machine learning models that power our client communications.What will you do?Design, build, validate and deploy advanced marketing models related to programmatic advertising platforms, product recommenders, propensity models and prospect personalization models to maximize client acquisition and efficiency.Partner with RBC internal partners and lines of businesses (Digital Marketing, Programmatic Media) to help them frame use cases and define success metrics (Key Performance Indicators).Work primarily within our AWS environment (SageMaker, S3, Redshift) to accomplish business goalsPrepare, parse and integrate large and varied structured and unstructured data on AWS and on prem for model training and scoring while preserving privacy and unbiased estimates.Develop state of the art machine learning models end-to end, including feature engineering, model training, model inference, model production, model monitoring and model maintenance.Document and Validate model design and performance to ensure quality and scalability of all modelsMonitor model performance and business success metrics over time.Be responsible for researching new capabilities and technologies to drive innovationWhat do you need to succeed?Must-haveMaster’s degree in Statistics, Economics, Marketing (Quantitative Workstream) Mathematics, or related quantitative field3+ years experience in marketing science/data science in digital marketing, sales, eCommerce, or related fieldStrong foundation in statistics, probability, and machine learning (including deep learning), especially in areas like: Causal inference, regression analysis, hypothesis testing, Bayesian inference, time series forecasting, propensity modelling, attrition modelling, attribution, segmentation, CLV, and behaviour analysis,Strong programming skills in python (preferred) or R, including relevant statistical, machine learning, and deep learning libraries (pandas, numpy, stats models, scikit-learn, tensorflow/pytorch, etc.)Good foundations in SQL, big data frameworks (Apache Spark, Hadoop, Hive, etc.), and AWS Cloud (SageMaker, S3, Redshift) or Azure/GCP equivalentsAble to clearly convey technical concepts and results to both technical and non-technical audiences, including senior leadershipNice to haveStrong knowledge machine learning application design. Experience deploying models into productions with MLOps.Experience with on device learning and modelling.Experience in running A/B testing experiments in production. Experience with relational databases. Domain specific knowledge of marketing campaigns.Experience in financial services industry with broad understanding of marketing, product management, sales, finance, pricing and risk management is preferredPublications in machine learning and artificial intelligence.Passion for ethical AI and experience in algorithm transparency and interpretability.What’s in it for you?We thrive on the challenge to be our best, progressive thinking to keep growing, and working together to deliver trusted advice to help our clients thrive and communities prosper. We care about each other, reaching our potential, making a difference to our communities, and achieving success that is mutual.A comprehensive Total Rewards Program including bonuses and flexible benefits, competitive compensation, commissions, and stock where applicableLeaders who support your development through coaching and managing opportunitiesAbility to make a difference and lasting impactWork in a dynamic, collaborative, progressive, and high-performing teamA world-class training program in financial servicesOpportunities to do challenging work, to take on progressively greater accountabilities and to building close relationships with clientsAccess to a variety of job opportunities across business and geographies.#LI_Hybrid#LI-Post#LI-PK#TECHPJJob Skills Actuarial Modeling, Big Data Management, Commercial Acumen, Data Mining, Data Science, Decision Making, Machine Learning, Natural Language Processing (NLP), Predictive Analytics, Python (Programming Language)Additional Job DetailsAddress: RBC WATERPARK PLACE, 88 QUEENS QUAY W:TORONTOCity: TORONTOCountry: CanadaWork hours/week: 37.5Employment Type: Full timePlatform: PERSONAL & COMMERCIAL BANKINGJob Type: RegularPay Type: SalariedPosted Date: 2024-10-04Application Deadline: 2024-10-18Inclusion and Equal Opportunity EmploymentAt RBC, we embrace diversity and inclusion for innovation and growth. We are committed to building inclusive teams and an equitable workplace for our employees to bring their true selves to work. We are taking actions to tackle issues of inequity and systemic bias to support our diverse talent, clients and communities.We also strive to provide an accessible candidate experience for our prospective employees with different abilities. Please let us know if you need any accommodations during the recruitment process.Join our Talent CommunityStay in-the-know about great career opportunities at RBC. Sign up and get customized info on our latest jobs, career tips and Recruitment events that matter to you.Expand your limits and create a new future together at RBC. Find out how we use our passion and drive to enhance the well-being of our clients and communities at .

Expected salary

Location

Toronto, ON

Job date

Sun, 06 Oct 2024 04:25:23 GMT

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