Junior Data Scientist
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Summary about this job
Analysts
Company: Correlate Resources
Location: Sydney
Work type: Full Time
Salary: $70-90k pa
Phone: +61-2-6723-3125
Fax: +61-8-8032-2882
E-mail: n\a
Site: n\a
Detail information about job Junior Data Scientist. Terms and conditions vacancy
- INDUSTRY PIONEERS: Disrupting the market with cutting edge Customer Data Science
- CUSTOMER DATA SCIENCE: Strategy Modelling, Data Science, Insights, Visualisation
- DATA & TECHNOLOGY: R, Python, SQL, SAS, Spark, Scala, Hadoop, Tableau, Qlikview
As a Junior Data Scientist the responsibilities of this role will involve but not be limited to:
- Work within a supportive team of industry pioneers on Blue Chip clients to map out complex customer strategies to be optimised through Data Science and Machine Learning techniques.
- Leverage strong programming skills in Python, R, SQL or SAS to manage and manipulate and supliment large volumes of customer data in preparation for modelling and analysis assignments.
- Develop creative and intuitive, insights driven dashboards leveraging cutting edge data visualisation tools such as Tableau, Qlikview, PowerBI, etc.
- Support delivery of a range of bespoke Data Science, Machine Learning, Statistical Modelling led projects leveraging techniques that may include; Linear/Logistic Regression, Confidence Interval, Test of Hypotheses, Clustering - Unsupervised & Supervised Learning, Time Series, Decision Trees , Monte-Carlo Simulation, Bayesian Statistics, Principal Component Analysis, Neural Networks, Gradient Boosting, Nearest Neighbors, (Geo-) Spatial Modeling, Recommendation Engines, Attribution Modeling, Segmentation, Predictive Modeling, Scoring Engine, Survival Analysis, Lift Modeling, Yield Optimization, Cross-Validation, Model Fitting, Experimental Design, etc.
- Experience using R, Python, SQL or SAS to extract, manipulate and merge large customer behaviour or transactional data sets from a variety of source systems (SQL Teradata, Oracle SQL, SQL Server, AWS, SAS Base or R).
- At least 1yr of commercial experience extracting 'Customer Insights' from large and complex datasets relating to; customer strategy (acquisition, retention & growth), statistical modelling, marketing campaigns or credit risk analysis, etc.
- Educated to a minimum of degree level in an analytical discipline such as - Mathematics, Statistics, Econometrics, Actuarial Studies, Data Science, Computer Science etc.
- A genuine passion to build a career in Data Analytics / Data Science, implementing analytical techniques and concepts such as; Linear/Logistic Regression, Confidence Interval, Test of Hypotheses, Clustering - Unsupervised & Supervised Learning, Time Series, Decision Trees, Monte-Carlo Simulation, Bayesian Statistics, Principal Component Analysis, Neural Networks, Gradient Boosting, Nearest Neighbors, (Geo-) Spatial Modeling, Recommendation Engines, Attribution Modeling, Segmentation, Predictive Modeling, Scoring Engine, Survival Analysis, Lift Modeling, Yield Optimization, Cross-Validation, Model Fitting, Experimental Design, etc
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