Role and Responsibilities
The Assoc Data Science Analyst builds data products that extract insights from datasets. He retrieves and analyzes diverse datasets, and assesses the quality and perform transformations to prepare the data to be ready for more advanced analysis.
He/She applies statistical, algorithmic, mining and visualisation techniques on datasets and develop model of high technical quality. He assists with setting up environments conducive for analysing data, develop and document analysis scripts, summarising results and developing conclusions . He possesses a combination of analytic, machine learning, data mining and statistical skills as well as experience with algorithms and coding.
He has interest in uncovering patterns in underlying complex data. He possesses data intuition and curiosity to learn and master application of new techniques on diverse real-world data, and tenacity to solve problems when encountering new unknown scenarios.
Prepare data sets
Extract data from data sources
• Integrate multiple data sets to build large and complex data sets
• Assess and report data quality with recommendations on fit for analytics use
• Apply programming abilities to build software to scrub, combine, and manage data from a variety of sources
• Develop and update data dictionary of data relevant for further analysis
Apply data mining techniques and programming skills to investigate leads, identify patterns and regularities in data
• Develop machine learning prototypes and minimum viable products to evaluate viablity of models
• Maintain reproducibility and understanding of the modeling process via documentation and other means
Assist with the development of actionable recommendations
• Develop compelling, logically structured presentations including story-telling of research/analytics findings
Masters in a quantitative field such as Mathematics, Statistics, Information Technology, Physics, Supply Chain Management, Operations Research, Engineering, Finance.
At least 3 years of relevant working experience.
Knowledge of statistical and data mining techniques, which may include some of the following: predictive modeling, regressions models, cross section time series models, longitudinal data analysis, evidence based modelling and various Clustering algorithms
Working experience with R and/or Python with models deployed for operational use.
Proven communication skill to explain insights from technical work to non-technical audience through presentation or other means
In depth knowledge in two or more of the following area is advantageous –
a) Data Profiling/ SQL querying
b) Data Quality
c) ETL and Data warehousing
d) Data Governance and Healthcare data standards
e) Statistical modelling
f) In memory analytics
g) Big data framework (e.g. Hadoop, Spark)