My career goal now would be a machine learning engineer or a data scientist in a Tech company. My area of interest revolves around AI and robotics thus my vision of my future would be working in a pioneer tech company that specialized in AI with implementation of robotics. Thus I need to attain more hands-on experience and knowledge through other jobs that is available for fresh graduate entry level in order to reach my goal.
The Below are the requirements of a machine learning engineer
Responsibilities
- Study and transform data science prototypes
- Design machine learning systems
- Research and implement appropriate ML algorithms and tools
- Develop machine learning applications according to requirements
- choose appropriate datasets and data representation methods
- machine learning tests run and experiments
- Perform statistical analysis and fine-tuning using test results
- Train and retrain systems when necessary
- help Increase existing ML libraries and frameworks
- Keep abreast of developments in the field
Skills and Requirements
- Proven experience as a Machine Learning Engineer or similar role
- Understanding of data structures, data modeling and software architecture
- In depth knowledge of math, probability, statistics and algorithms
- Have strong ability in Python, Java and R
- familiar with machine learning frameworks (eg. Keras or PyTorch), and libraries (eg. scikit-learn)
- Strong communication skills
- Efficient teamwork ability
- analytical and problem-solving skills
- Bachelor degree in Computer Science, Mathematics or similar field; Master’s degree is a plus
The below are the requirements of a data scientist
Responsibilities
- Selecting features, building and optimizing classifiers using machine learning techniques
- Utilization of state-of-the-art methods in data mining
- Sharing company’s data with third party sources of information when needed
- Enhancing data collection procedures to include information that is relevant for building analytic systems
- Processing, cleansing, and verifying the integrity of data used for analysis
- Provide analysis on demand and ability to give an clear presentation
- building an automated anomaly detection systems and continuously monitor its performance
Skills and Qualifications
- understanding of machine learning techniques and algorithms (eg. k-NN, Naive Bayes, SVM, Decision Forests, etc)
- Have experience in handling common data science toolkits (eg. R, Weka, NumPy, MatLab, etc)
- Good communication skills
- Experience with data visualisation tools (eg. D3.js, GGplot)
- Proficiency in using data base computer languages (eg. SQL, Hive, Pig)
- Experience variety database other than SQL database (eg. MongoDB, Cassandra, HBase)
- Good at applying statistics skills (eg. distributions, statistical testing, regression)
- Data-oriented personality
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