Biomedical Scientist jobs in Singapore

The Lead Data Scientist applies strong expertise in machine learning, data mining, and information retrieval to design, prototype, and build next generation advanced analytics engine and services. The Lead data scientist collaborates with translators to define technical problem statement and hypothesis to test; develops efficient and accurate analytical models that mimic business decisions.

1. Design, create, test and implement complex models and algorithms that drive analytical solutions throughout the organization.
2. Conduct advanced statistical and other analysis to provide actionable insights, identify trends, and measure performance.
3. Collaborate with translators and understand business problems to implement scalable and sustainable solutions.
4. Coordinate with Data Engineers to deliver holistic analytical solutions.
5. Support translators in communicating the design, functioning and output of the analytical models/solutions developed.
6. Ensure timely analysis and testing for regular maintenance of solutions over time.
7. Lead cross-functional projects using advanced data modeling and analysis techniques to discover insights that will guide strategic decisions and uncover optimization opportunities.
8. Build, develop and maintain data models, reporting systems, data automation systems, dashboards and performance metrics support that support key business decisions.
9. Develop and deliver data science courses as part of advance analytics and digital academy.
10. Able to describe analytic processes from start to finish in his area of expertise.
11. Strong experience using a variety of data mining/data analysis methods, using a variety of data tools, building and implementing models, using/creating algorithms and creating/running simulations.
12. Develop processes and tools to monitor and analyze model performance and data accuracy.
13. Perform other duties as assigned.

Minimum Qualifications:

1. BSc or preferably MS/PhD (Computer Science or Electronics), or MS in Computer Applications, Statistics or Mathematics or related technical discipline.
2. Minimum Programming languages knowledge in Python, R, Java and SQL.

Minimum Experience:

1. 5+ years’ relevant experience in data science, statistical and business intelligence role.
2. Should have domain knowledge of metal and mining or energy or heavy industry or oil and gas experience or a combination of these industries.
3. Experience in machine learning, statistics, optimization or related fields; deeper experience in working with large data sets, simulation/optimization or distributed computing tools is a plus.
4. Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
5. Experience of working with large data sets relational databases (SQL), simulation/optimization and distributed computing tools (Map/Reduce, Hadoop, Hive, Spark, MySQL etc.) is required.
6. Experience of visualizing/presenting data for stakeholders using: Periscope, Business Objects, D3, Power BI, Tableau etc. is required.
7. Knowledge of additional programming languages is a plus (C++, Java, SPSS, SAS, Scala).
8. Working knowledge of data mining principles: predictive analytics, mapping, collecting data from multiple data systems on premises and cloud-based data sources.
9. Data treatment/Data mining, e.g. SQL, AWK, Access, Spark, Excel is required.
10. Strong knowledge of one or more data science or decision science domains (e.g. explainable artificial intelligence, econometrics, deep learning, Natural Language Processing, time series forecasting, deployment, causal inference, uplift modelling, and/or optimization).
11. Knowledge of distributed computing or NoSQL technologies is a bonus.
12. Experience working with and creating data architectures.
13. Experience in automation & control system and training in similar fields is valued.
14. Good working knowledge of industry 4.0 application, big data, autonomous robots, system integration, cloud computing, additive manufacturing, augmented reality etc.

1. Deep knowledge of machine learning models/techniques (incl. setup, structure, run, stabilize and implement models).
2. Strong understanding of Agile methodologies.
3. Openness to working in Agile environments with multiple stakeholders.