行业：Sciences
职位：Actuarial / Statistics
部门：
人数：若干
性质：Full Time
性别：Male
婚姻：No marital status restrictions
学历： Master's Degree
经验：不限
年龄：18岁以上
待遇：面议
Qualifications:
1. Minimum of six (6) years’ experience in customer analytics / data science domain, covering most of the following: data mining, predictive modeling, machine learning, statistical modeling and analysis, large scale data acquisition, transformation, and cleaning, both structured and unstructured data
2. Proven track record of leading and collaborating on advanced analytics strategic initiatives
3. Proven track record of operationalization of analytic models in collaboration with marketing/risk and IT teams
4. Worked with large, unfiltered data sets or data science research
5. Successfully led a data science/data analytics team
Knowledge of:
1. Degree in quantitative discipline such as Statistics, mathematics, Operations Research, Engineering, Computer Science, Econometrics or Information Science such as Business Analytics or Informatics
2. Has Knowledge of both structured and unstructured data
3. Must possess core competencies, deep understanding and relevant experience in: a. scripting or programming experience: familiarity in programming languages with relational databases (e.g. Python, R, Java, Spark, SQL) b. Statistical Analysis: advanced usage of Machine Learning toolsets such as AWS Sagemaker, GCP ML, and the likes c. Big data: Experience with Big data tools such as Hadoop, Snowflake, etc. d. Database knowledge: skilled in structured database
4. Familiar with most of the following disciplines:
a. Conceptual modeling: to be able to share and articulate modeling;
b. Predictive modeling: most of the big data problems are towards being able to predict future outcomes;
c. Hypothesis testing: being able to develop hypothesis and test them with careful experiments;
d. Natural Language Processing: the interactions between computer and humans;
e. Machine learning: using computers to improve as well as develop algorithms;
f. Statistical analysis: to understand and work around possible limitations in models.
g. Image Recognition, Object Detection and the likes