Lead Data Scientist Resume

SUMMARY

4+ years’ hands - on experience in Analytics and Masters’ in Business Analytics degree with focus on machine learning and statistics.
Led a team to create build machine learning models and created roadmap to in corporate models into workflow.
Experience building machine learning models such as Random Forests, k-NN, logistic regressions and using packages such as ggplot, dplyr, numpy, sci-kit learn, pandas, etc.
Reported analytical findings to C-level executives using dashboards built in Tableau, Qlik View, R-Shiny which involves analyzing millions of data points from SQL, Hadoop databases.
Built and managed teams to perform data analysis, classification and forecast models, text mining, sentiment analysis, statistical models, risk analysis and solved complex data driven problems using R, SAS, Python, SPSS, SAS E-Miner, E-Views, Tableau, Qlik, etc.
Established predictive sales leads, automation processes, feedback loops, platform integrations, optimization models, models to increase user experience, A/B testing.
Designed A/B tests to determine the efficiency of email, web marketing campaigns and tracked the performance of KPIs using Google Analytics and Adobe Site Catalyst.

PROFESSIONAL EXPERIENCE

Lead Data Scientist

Confidential

Responsibilities:

Build classification models based on advisor performance and deduced knowledge rule for high, medium performing advisors.
Deduced treatment plans for advisors by inspiring from interactions of high performing advisors.
Prioritized leads and created nurturing journey based on learnings from previous modelling practices.
Coordinated with visualization team to create dashboard to compare and monitor performance of models.
Identified data vendors to optimize the performance of existing models and pave way to build new models.

Big Data Scientist/Analyst

Confidential

Responsibilities:

Launched predictive leads for sales and established predictive feedback loop to increase efficiency of predictive models across US, Asia-Pacific and Greater China regions.
Build Central Tableau dashboard to compare the performance of predictive models across all regions.
Work with campaign managers to understand their campaign targets, determine the size of data necessary to meet targets and provide analysis on various conversion metrics.
Automated digital marketing campaign creation process & reduced manual hours by 80%.
Processed data close to 15 million rows, discovered trends in data and analyzed ways to utilize data to solve complex data driven problems to support various use-cases.
Track down customer journey from AQL (Auto Qualified Leads) to SQL.
Analyze different reasons to accept/reject as MQL and strategize ways to utilize rejected data.
Provide a detailed dashboard analysis on data pools, conversion metrics, cross-regional performance, timelines and reason for outperformance by extracting data from Hadoop and SQL database.
Identify ways to scale projects, optimize existing process and create new use-cases.

Tools: SQL, Tableau, R, Python, Hadoop, Hive.

Data Scientist

Confidential

Responsibilities:

Used ggplot2, dplyr, lm, e1071, rpart, randomForest, nnet, tree packaged in R to built predictive models for Insurance and healthcare clients and successfully incorporated models into workflows.
Built predictive models to predict churn of customers & ‘Next product to buy’ models using logistic regression and neural nets respectively using SPSS and SAS.
Managed many analytical projects in parallel such as build predictive models, optimization models, unstructured data analysis, data graphs and sentiment analysis.
Extracted social media data, crunched and built word clouds, data graphs and story boards using SAS E-Miner. Provided in-depth story analysis and provided recommendations.
Designed web marketing, planted and analyzed java tags and tracked performance using Google Analytics.
Helped companies with social media analysis, performed text mining, sentiment analysis and presented the results using Link Graphs in SAS E-Miner.