We are seeking an experienced equity trader to join our trading desk, with a strong emphasis on data analytics, electronic execution, and the use of computational and AI-driven tools to optimize trading performance. This role is ideal for a trader who combines market execution expertise with technical fluency - someone who can analyze execution data, build tools to improve workflow efficiency, and leverage algorithmic and AI-based platforms to drive better outcomes for clients.
Key Responsibilities and Duties- Execute equity trades across multiple venues, using both manual and algorithmic strategies based on order characteristics and market conditions
- Analyze execution quality using TCA data and benchmarks, identifying trends and opportunities to improve performance
- Build and maintain tools (Excel macros, Python/SQL scripts) to automate data analysis, reporting, and workflow efficiency
- Evaluate and help integrate AI-driven or algorithmic tools into the desk's execution process
- Partner with clients and internal stakeholders to communicate execution results and data-driven insights clearly and effectively
- Collaborate with technology and quant teams to refine algo strategies and execution tools based on data findings
Educational Requirements- University (Degree) Preferred
Work Experience- 3+ Years Required; 5+ Years Preferred
Physical Requirements- Physical Requirements: Sedentary Work
Career Level 7IC
Required Qualifications- 3 years of direct equity execution and trading experience
- Hands-on experience with electronic execution platforms and algorithmic trading strategies (e.g., VWAP, TWAP, POV, liquidity-seeking algos)
- Working knowledge of TCA (Transaction Cost Analysis) metrics and benchmarks (e.g., arrival price, implementation shortfall, VWAP) and the ability to apply this data to improve execution decisions
- Advanced Excel proficiency, including macros/VBA, for building models and automating recurring analysis
- Programming proficiency in Python, SQL, or VBA, with demonstrated experience building tools, scripts, or dashboards to support trading or data analysis
Preferred Qualifications- Bachelor's degree in a quantitative field (Engineering, Computer Science, Math, or related discipline)
- Experience applying AI or machine learning tools to trading workflows (e.g., predictive analytics, pattern recognition in execution data, AI-assisted algo selection or parameter tuning)
- Familiarity with building or customizing dashboards/visualizations (e.g., Python libraries, Power BI, Tableau) to communicate execution performance to internal stakeholders or clients
- Knowledge of US equity market structure and relevant regulations
- Experience with EMS/OMS platforms and API-based connectivity to trading systems
- Strong interpersonal and communication skills, with experience managing client relationships and explaining technical/data-driven insights to non-technical stakeholders
- Ability to integrate and contribute within a team-oriented environment
Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable state and local laws, including the City of Los Angeles Fair Chance Initiative for Hiring ordinance and the San Francisco Fair Chance Ordinance.
Related Skills
Communication, Consultative Communication, Continuous Improvement Mindset, Data Analysis, Due Diligence, Equity Trading, Executive Presence, Financial Markets, Inspires Others, Market/Industry Dynamics, Relationship Management, Trading Best Execution
Anticipated Posting End Date:2026-10-24
Base Pay Range: $150,000/yr - $185,000/yr
Actual base salary may vary based upon, but not limited to, relevant experience, time in role, base salary of internal peers, prior performance, business sector, and geographic location. In addition to base salary, the competitive compensation package may include, depending on the role, participation in an incentive program linked to performance (for example, annual discretionary incentive programs, non-annual sales incentive plans, or other non-annual incentive plans).
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