Senior Data Scientist

ESSENTIAL DUTIES AND RESPONSIBILITIES:

As a Data Scientist, you will be working on Digital Innovation projects leveraging 4IR and cutting-edge technologies. You will be collaborating with Data Science Team to design and build analytics tools/models/analytics to accelerate digital technologies adoption and unlock business value from data insights. You are required to constantly learn new front-tier technology and creatively apply/deliver solutions to support Global Ops Strategy and goals.

• Work with a team of high-performing analytics, data science professionals, and cross-functional teams to understand business problem, identify business opportunities, optimize business performance and/or improve KPIs
• Analyze large-scale structured and unstructured data; develop deep-dive analysis and machine learning / AI models to drive business value and improve KPIs.
• Leverage data mining techniques in mathematics, statistics, machine learning, deep learning, NLP, LLM, GenAI visualization etc. to discover insightful patterns.
• Develop/take an idea, access and prepare necessary data to create prototype machine learning models/algorithms, develop it to an application with intuitive user interface, integrate with any pre-existing systems, demonstrate successful use cases and wins.
• Develop and code, software programs, algorithms, typically on very large datasets, from multiple sources, including IoT devices/sensors.
• Interprets actionable insights from large data and metadata sources and communicates the findings to product, service, and business leaders for product improvement
• Prepare and deliver presentations with data visualizations and business conclusions
• Provide technical guidance and mentorship to junior team members on solution design

Requirements

REQUIRED:

• Bachelor's Degree or Master's Degree in related fields and research projects related to Analytics/Data Science with relevant industry or academia experience.
• Background in at least one programming language (eg. R, Python, Java, Scala/Spark)
• Experience with SQL, Relational databases, Big Data platforms, AWS, etc.
• Experience in applied statistics and statistical modeling. Practical experience in the application of ML and AI algorithms.
• Experience with Hadoop or other MapReduce paradigms, and associated languages such as Hive, Presto, etc.
• Experience in models’ deployment and E2E model management through docker image, container, MLOps.
• Experience working with structured, semi-structured, and unstructured data sources. Familiarity with common data modeling approaches and good understanding of how to deal with various datatypes, larger data sets and parallel computing problems.
• Experience in solution architecture, its technology stacks, and components.

PREFERRED:

• Excellent knowledge of at least one of the following programming languages / frameworks: Python, R, Java, SQL, Scala
• Excellent knowledge of the AI/ML Model development, Lifecycle Management (Modelling, Integration/Deployment, Data/Model drift detection, Model retraining, etc.)
• Up-to-date knowledge and skills in recent Machine Learning tools and techniques such as Deep Learning, NN, NLP, LLM, etc
• Knowledge of web front-end development: HTML/CSS, JavaScript (JQuery or Angular preferred)
• Knowledge of Hadoop, including Hive, Map Reduce, No/SQL, HBase, and Spark
• Familiar with collaborative solutions, model & code versioning (Github), solution packaging (Docker)
• Practical experience of cloud-based solutions is a strong plus.
• Aptitude to interact with functional or business stakeholders who are not familiar with ML Engineering considerations.
• Passion for Innovation, continue learning new techniques. Monitor the market for emerging technologies for adoption and work on proof of concepts then scale for full deployment.

SKILLS:

• Strong communication, analytical and creative problem-solving skills
• Strategically focused, impact-oriented, highly organized, and adaptable.
• Strong knowledge and experience with R, Python, or other statistical software
• Familiarity with core techniques in statistical and machine learning e.g. regularized regression, time series analysis, tree base models, boosting algorithms, frequentist and Bayesian inference, clustering, cross-validation, NN, NLP, LLM, and data visualization.
• Knowledge in web crawling, NLP, visualization, and dashboard creation
• Ability to solve problems and provide complex solutions with limited direction.
• Ability to deploy data science solutions in cloud analytics infrastructure and AWS
• Analytical mind and business acumen. Application understanding of machine-learning and operations research
• Proactive and collaborative effectively in a rapidly changing environment (VUCA), a great team player.
• Results-oriented, with growth and imaginative mindset and strong dedication and passion to identify and implement improvement opportunities. Strong drive for results.
• Excellent communications and presentation skills, with the ability to synthesize, simplify and explain complex problems to different types of audiences, including executives.