Data Scientist

Job Description

• Integrating
• Machine Learning (ML)
• with a focus on
• real-world data
• experience adds a significant layer of technical depth to this role.
• Willing to work on a roster system: 4 weeks on, 2 weeks off.
• You will work closely with Team in identifying potential data science solutions.

Consolidate, verifying, analyzing, from operational data, transaction data, marketing data, and external data to develop hypotheses and building potential quick win use case or business improvement recommendation

Hand in hand with data engineering and BI team identifying potential available data, for developing the business DataMart, build the models, train the model, validate, test it and fine tuning on real world use case

In charge and comfortable in model presentation, articulating, communicating in concise, effective and clear communication during the model development towards line of business, risk team, and technical team.

• Collaborate with business and tech teams to define data-driven strategies.
• Interpret data insights and present findings to non-technical stakeholders.
• Training and Certification related to Data Science or Data Analytic.
• Recommend platforms, frameworks, and technologies for data science.
• Provide input on data strategy and product development decisions.

Qualification

• Bachelor's in Technical Information / Information System / Industry Engineering or any major with minimum GPA 3.00 (on a scale of 4.00).
• Minimum 3-5 years experience in the same field.
• Have great knowledge in Java, Python with experience to apply
• Machine Learning frameworks
• and libraries (TensorFlow / Keras / Spark MLlib / Sklearn / pandas, etc).
• Willingness to be deployed/stationed at Client Sites located outside of Java Island for the duration of project assignments.

Basic to Intermediate knowledge in some of data science methodologies either classic or black box including but not limited to classical regression, neural network, association rules, sequence analysis, classification, cluster analysis, gradient boost, text mining, etc.

Solid analytical and problem-solving skills to interpret, derive and create data-driven insights

Actively contribute in all aspects of ML model development: data wrangling, feature engineering, model selection / architecture, training, offline evaluation, plans A/B experimentation & roll out (production)

Passionate and/or practicing in data technologies (Big Data / ETL / Visualization / AI / ML / Deep Learning, etc) and care with details, numbers, and data quality.