We are seeking a highly skilled and motivated Desk Aligned Engineer to join the research and development team within a large hedgefund focusing on state of the art fundamental & systematic strategies. This role focuses on developing and maintaining real-time data processing systems and analytical tools to generate short-term alpha opportunities.

 

The ideal candidate will have a strong background in software engineering, data analysis, and event-driven architecture, with a passion for leveraging alternative data sources to gain an informational advantage.

 

You will be working with a modern tech stack including Python (Numpy, and Pandas), Dask, GCP, Airflow, KDB.

 

Responsibilities:

• Design, develop, and maintain high-performance data pipelines for processing and analyzing real-time market data, news feeds, social media sentiment, podcasts, and other alternative data sources
• Implement and optimize event-driven architectures using technologies like Kafka to capture and react to market events with minimal latency
• Build and deploy scalable analytics solutions on cloud platforms (e.g., GCP) for handling large volumes of structured and unstructured data
• Develop and maintain a knowledge graph to represent complex relationships between market entities and events
• Apply natural language processing (NLP) and other machine learning techniques to extract meaningful insights from unstructured data (text, audio)
• Leverage causal inference methods to assess the impact of events on market prices and identify potential trading opportunities
• Collaborate closely with traders and quantitative analysts to understand their needs and translate them into effective technological solutions
• Continuously monitor and improve the performance, reliability, and scalability of the research infrastructure

 

Requirements

 

I. Data Structures

 

Basic Structures

• Dictionaries (Hash Maps)
• Sets
• Tuples

 

Advanced Structures

• Ring Buffer
• LRU Cache
• Priority Queue
• Skip List
• B-Tree
• Suffix Tree
• Trie
• Trees
• Graphs
• Heaps

 

II. Algorithms

 

Fundamental Algorithms:

• Sorting
• Searching
• Graph Traversal
• Dynamic Programming

 

Specialized Algorithms:

• Streaming algorithms
• Frequency Estimation
• Finding Frequent Items
• Quantiles and Order Statistics
• Sliding Window Algorithms
• Streaming Graph Partitioning
• Streaming Community Detection
• Graph Algorithms
• Pathfinding Algorithms
• Time Series Analysis
• Anomaly Detection Algorithms
• Forecasting Algorithms

 

III. Software Engineering Principles

• Object-oriented programming: Classes, inheritance, polymorphism
• Memory management: (garbage collection, optimization)
• Clean code: (readability, maintainability, documentation)
• Testing: (unit tests, integration tests, mocking)
• Version control: (Git and GitHub)

 

IV. Software Architecture and Design

• Event-driven architecture
• Concurrency and parallelism (Dask, threads, processes, asyncio)

 

V. Tools and Technologies

 

Data Science Tools

• NumPy
• Pandas

 

Big Data and Cloud Computing (GCP) Core services

• Compute Engine
• Cloud Storage
• BigQuery

 

Deployment and orchestration

• Kubernetes, Docker

 

Messaging Systems:

• Kafka

 

Qualifications:

 

Academically brilliant:

• Demonstrate top-tier performance in a Computer Science & Applied Maths from a leading institution
• Class Rank or GPA (e.g., top 5% of class, 3.8 GPA)

 

Industry veteran

• Possess at least two years of experience in a hedge fund or technology firm, showcasing your ability to handle complex data challenges and thrive under pressure
• Proactive and driven: Take initiative, assume increasing responsibility, and consistently exceed expectations
• Proven track record of growth: Highlight your career progression and advancements, demonstrating your commitment to excellence and professional development