Taj Anderson

Areas Taj Anderson is Knowledgeable in:

Taj Anderson's Problem Solving Skills:

1. Solution Architect

Taj Anderson's Problem Solving Experience:

1. ● Analyze the cytotoxic effects on the Annona glabra (pond apple) seed on Breast Cancer cell lines: MCF 7(estrogen dependent) and MDA 231(estrogen independent).
   ● Conduct cell culturing and treatment analyzation as well as the quantification of cytotoxicity after drug application using MTS assay and MMA assays.
   ● Apply downstream applications such as HPLC and Mass Spectroscopy to isolate and produce structure responsible for the cytotoxic effect.
   ● Reported cytotoxicity in MDA 231 at concentrations below pharmaceutical industry grade levels (approx. 0.001 ug/ml), while control cells were unaffected by treatment.
2. ● Forecast contact volume and cost across all lines of service and adjust models for accuracy while maintaining KPI goals and flexibility in models to adjust to current business needs. Adjustments to models through introduction of new variables have led to 10% increase in agent utilization YTD.
   ● Weekly analysis and escalation of high risk cases to the Quality Team through the use of an early detection system protocol that flags anomalies within the consumer product data. Currently working on machine learning algorithm to enhance current anomaly detection capabilities.
3. ● Weekly ad-hoc delivery of consumer insights through Salesforce reporting and analysis of CRM database(1,000,000+ contacts) across all A&C brands with data feeds and deliveries to the Marketing, Quality, Research Development, and Legal Departments.
4. ● Search Engine Data Integration into Algorithmic Trading Models to predict market fluctuations to reduce risk and ultimately increase alpha returns in applicable markets. The goal is to integrate all aspects of social web based information to further increase “human like attribute” of machine learning techniques applied in computational investing.
   ● Machine learning algorithm that quantifies research quality in biotech emerging markets by assigning a numerical score (e.g. 1 to 10) based on experimental design and feasibility of downstream application.