Pradeep Parida

I am a Data Scientist with 2+ years of experience in applying advanced analytics, predictive modeling, and statistical methods to solve complex business problems, with a strong focus on marketing, customer behavior, and operational optimization. My background combines technical expertise in Python, SQL, and cloud platforms (AWS, GCP), with business-driven data science applications that have delivered measurable results, including improving customer retention by 25% and enhancing predictive model accuracy by 20%.

In my current role at CSW Alliance,

• Built NLP-driven sentiment analysis models (89.3% accuracy) for customer feedback.

• Designed predictive models for buying trends (+15% forecast accuracy).

• Created churn-prediction frameworks to guide retention strategies.

• Developed ETL pipelines and executive dashboards that enabled data-driven decision-making.

Previously, as a Data Analyst at TELUS International,

• Achieved 95%+ accuracy in annotating 1,000+ LiDAR and image datasets.

• Reduced data rework by 20% with structured QA processes.

• Contributed to global AI annotation workflows across multiple modalities and languages.

Additionally, my previous experience as a sales engineer has equipped me with exceptional customer relationship management skills, allowing me to effectively communicate complex information to non-technical stakeholders.

Beyond client work, I actively contribute to the data science community by mentoring aspiring data professionals, participating in competitions (Top 10% in Netflix Data Insights, Top 5% in StackUp ML competition), and continuously building projects in predictive analytics, NLP, and AI agents.

I am particularly interested in applying analytics to marketing, pricing, and promotions, leveraging hypothesis testing, scenario modeling, and predictive techniques to generate insights that directly impact business growth. My career goal is to continue developing as a data scientist in consulting and applied analytics, contributing to high-impact projects at the intersection of data, business, and technology.