SLICIT is on the forefront of integrating Web 3 0 technologies into the asset management and fintech sectors We are seeking a Data Scientist with a pioneering spirit to harness vast datasets and apply analytical rigor in the realms of blockchain tokenization NFTs and other Web 3 0 concepts Your role will be instrumental in driving innovation through data-driven insights particularly in optimizing asset management strategies exploring fintech opportunities and leveraging real-world assets through digital transformation This position is tailor-made for someone who not only excels in technical and analytical capacities but also brings a wealth of business experience and industry knowledge in asset management fintech and the burgeoning field of digital assets Reporting to Chief Technology Officer Employment Type Full-Time
Machine Learning and AI Expertise
The ideal candidate will possess a robust foundation in both theoretical and practical aspects of machine learning ML and artificial intelligence AI with a particular emphasis on applications within the financial sector You should have hands-on experience designing implementing and validating a wide range of ML models from traditional algorithms like Linear Regression Decision Trees and Clustering to more advanced techniques including Neural Networks Natural Language Processing NLP and Reinforcement Learning
Knowledge of Tools and Frameworks
Proficiency in using Python libraries such as Scikit-learn for machine learning model development and TensorFlow or PyTorch for deep learning applications Experience with NLP tools like NLTK or spaCy to derive insights from textual data which is particularly relevant in analyzing market sentiment or financial news Familiarity with reinforcement learning frameworks such as OpenAI Gym which can be applied to develop sophisticated trading algorithms that adapt and learn from the market s dynamics A track record of employing AI-driven analytics to forecast market trends optimize investment portfolios or enhance customer personalization and engagement in fintech solutions
Demonstrated Impact
Evidence of past projects where your ML and AI expertise directly contributed to significant business outcomes such as improved investment returns risk reduction operational efficiencies or customer satisfaction enhancements The ability to quantify the impact of your work e g percentage increase in investment performance reduction in risk exposure will be highly valued
Programming and Data Manipulation Proficiency
Adeptness in programming and data manipulation critical for analysing complex datasets and developing scalable solutions
Core Programming Languages
Proficiency in Python is essential given its widespread use in data science for everything from data manipulation with Pandas to complex machine learning with libraries like TensorFlow and scikit-learn Expertise in R is also valuable particularly for statistical analysis and visualization Knowledge of SQL is crucial for querying databases and manipulating data stored in SQL databases
Data Manipulation Tools and Techniques
Candidates should demonstrate expertise in handling large datasets employing techniques and tools for data cleaning transformation and aggregation Experience with Python libraries such as NumPy and Pandas or R packages like dplyr and tidyr is expected
Version Control and Collaboration
Familiarity with version control systems notably Git is necessary for efficient collaboration within a team This includes managing code repositories branching merging and handling pull requests in platforms like GitHub or GitLab
Advanced Analytical and Statistical Skills
Expertise in statistical analysis predictive modeling and data mining with a proven track record of leveraging these skills to drive business decisions
Education
A Master s or PhD in Data Science Computer Science Mathematics Statistics or related field with ideally coursework or projects focused on finance blockchain or digital assets
Programming and Data Manipulation Proficiency in Scala or Java
would be a plus tailored towards data science applications in finance and asset management
Big Data Technologies
Familiarity with big data frameworks and cloud computing environments enhancing data processing and analysis capabilities
Deep Learning Proficiency
Knowledge of deep learning applications in finance such as predictive analytics for market trends or customer behaviour analysis
Data Visualisation Skills
Competence in using tools like Tableau or Power BI for creating intuitive data visualisations that convey complex information to stakeholders
Business Experience and Industry Knowledge
Some experience in asset management fintech or related sectors with a keen understanding of market dynamics regulatory environments and the potential of digital transformation through Web 3 0 technologies
Blockchain and Web 3 0 Technologies
Deep understanding of blockchain fundamentals smart contracts tokenization of assets NFTs and their implications for asset management and fintech is a big advantage Experience with platforms such as Ethereum Hedera Hashgraph and Polygon In particular
Fundamental Blockchain Concepts
Candidates should have a solid grasp of key blockchain principles including decentralized ledgers consensus mechanisms smart contracts and cryptographic protocols This knowledge is vital for analysing blockchain data and understanding the underlying technology of digital assets and transactions
Blockchain Platforms and Tools
Experience with major blockchain platforms such as Ethereum for smart contract development and ideally familiarity with newer platforms like Hedera Hashgraph Polygon or Polymesh
Smart Contracts and DApps Development
While primarily a data-focused role understanding the development and deployment of smart contracts and decentralized applications DApps can provide valuable insights into data sources and potential applications of blockchain in asset management and fintech Knowledge of Solidity for Ethereum or other smart contract languages is a plus
Machine Learning and AI Expertise: The ideal candidate will possess a robust foundation in both theoretical and practical aspects of machine learning (ML) and artificial intelligence (AI), with a particular emphasis on applications within the financial sector. You should have hands-on experience designing, implementing, and validating a wide range of ML models, from traditional algorithms like Linear Regression, Decision Trees, and Clustering, to more advanced techniques including Neural Networks, Natural Language Processing (NLP), and Reinforcement Learning. Knowledge of Tools and Frameworks: Proficiency in using Python libraries such as Scikit-learn for machine learning model development and TensorFlow or PyTorch for deep learning applications. Experience with NLP tools like NLTK or spaCy to derive insights from textual data, which is particularly relevant in analyzing market sentiment or financial news. Familiarity with reinforcement learning frameworks such as OpenAI Gym, which can be applied to develop sophisticated trading algorithms that adapt and learn from the market s dynamics. A track record of employing AI-driven analytics to forecast market trends, optimize investment portfolios, or enhance customer personalization and engagement in fintech solutions. Demonstrated Impact: Evidence of past projects where your ML and AI expertise directly contributed to significant business outcomes, such as improved investment returns, risk reduction, operational efficiencies, or customer satisfaction enhancements. The ability to quantify the impact of your work (e.g., percentage increase in investment performance, reduction in risk exposure) will be highly valued. Programming and Data Manipulation Proficiency: Adeptness in programming and data manipulation, critical for analysing complex datasets and developing scalable solutions. Core Programming Languages: Proficiency in Python is essential, given its widespread use in data science for everything from data manipulation with Pandas to complex machine learning with libraries like TensorFlow and scikit-learn. Expertise in R is also valuable, particularly for statistical analysis and visualization. Knowledge of SQL is crucial for querying databases and manipulating data stored in SQL databases. Data Manipulation Tools and Techniques: Candidates should demonstrate expertise in handling large datasets, employing techniques and tools for data cleaning, transformation, and aggregation. Experience with Python libraries such as NumPy and Pandas, or R packages like dplyr and tidyr, is expected. Version Control and Collaboration: Familiarity with version control systems, notably Git, is necessary for efficient collaboration within a team. This includes managing code repositories, branching, merging, and handling pull requests in platforms like GitHub or GitLab. Advanced Analytical and Statistical Skills: Expertise in statistical analysis, predictive modeling, and data mining, with a proven track record of leveraging these skills to drive business decisions. Education: A Master s or PhD in Data Science, Computer Science, Mathematics, Statistics, or related field, with ideally coursework or projects focused on finance, blockchain, or digital assets.
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