ML Engineer/Statistician/Data Scientist At Fulton, MD

VALSA TECH; Fulton (MD); On-site; USD 110,000 - 165,000; Full time

Job summary

VALSA TECH is seeking a Data Scientist to mine and analyze data from company databases, driving optimization of product development, marketing techniques and business strategies.

You will develop custom data models and ML algorithms, apply predictive modeling to improve customer experiences, revenue, risk management, and ad targeting, and assess new data sources for accuracy and utility.

Qualifications

• Proficiency in Python, R, SQL and statistical methods.
• Experience building data models and ML pipelines for production use.
• Strong ability to translate business questions into data-driven solutions.

Responsibilities

• Mine and analyze data from company databases.
• Develop custom data models and ML algorithms.
• Use predictive modeling to optimize business outcomes.

Skills

Python

R

SQL

Machine Learning

Statistics

Data Analysis

Data Visualization

Pandas

NumPy

Scikit-learn

TensorFlow

Education

Bachelor's degree or higher in a quantitative field

Tools

Hadoop

Hive

HDFS

MapReduce

Spark

MySQL

SQL Server

Oracle

Tableau

Power BI

Job description

Data Scientist

Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques and business strategies.

• Assess the effectiveness and accuracy of new data sources and data gathering techniques
• Develop custom data models and machine learning algorithms to apply to data sets
• Use predictive modeling to increase and optimize customer experiences, revenue generation, risk mitigation, fraud identification, ad targeting and other business outcomes

The candidate must demonstrate the following technical skills:

• Machine Learning, Artificial Intelligence, Statistical Modeling, Data Analysis, Predictive Analysis, Data Manipulation, Data Mining, Data Visualization and Business Intelligence
• Adept in statistical programming languages like Python, R and SAS including Big Data technologies like Hadoop, Hive, HDFS, MapReduce and NoSQL Based Databases
• Proficiency in Python data extraction and data manipulation, and widely used python libraries like NumPy, Pandas, and Matplotlib for data analysis
• Proficiency and experience in the use of using statistical computer languages (R, Python, SQL, etc.) to manipulate data and draw insights from large data sets
• Experience in training and testing data using various Machine Learning algorithms like Linear & Logistic Regression, Naïve Bayes, Decision Trees, Random Forests, Clustering, SVM, Neural Networks, Principle Component Analysis, and Bayesian
• Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world applications, advantages/drawbacks.
• Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications
• Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc.
• Knowledge of Recommender Systems
• Strong familiarity in working with various statistical concepts such as Hypothesis Testing, t-Test, and Chi - Square Test, ANOVA, Statistical Process Control, Control Charts, Descriptive Statistics and Correlation Techniques
• Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
• Strong interpersonal and communication skills

Preferred (but not mandatory) Skills and Experience:

• Knowledge of and experience in the banking/finance industry
• Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, etc.
• Familiarity with neural networks and deep learning techniques – RNN/LSTM, CNN, ANN
• Coding knowledge and experience with several languages
• Experience with data visualization software such as Tableau, Qlikview, MATLAB, Microsoft Power BI, etc.
• Experience analyzing data from third-party providers
• Knowledge and experience working in Agile environments including the Scrum process

Summary of Technical Skills – (A successful candidate should possess all or majority of these skills)

• Languages - Python, R, T-SQL, PL/SQL
• Packages/libraries - Pandas, NumPy, Seaborn, SciPy, Matplotlib, Scikit-learn, MLlib, ggplot2, Rpy2, caret, dplyr, RWeka, gmodels, NLP, Reshape2, plyr.
• Machine Learning - Linear Regression, Logistic Regression, Decision trees, Random forest, Association Rule Mining (Market Basket Analysis), Clustering (K-Means, Hierarchal), Gradient decent, SVM (Support Vector Machines), Deep Learning (CNN, RNN, ANN) using TensorFlow (Keras).
• Statistical Tools - Time Series, Regression models, splines, confidence intervals, principal component analysis, Dimensionality Reduction, bootstrapping
• Big Data Hadoop, Hive, HDFS, MapReduce, Pig, Kafka, Flume, Oozie, Spark
• BI Tools Tableau, Amazon Redshift, Birst
• Data Modeling Tools Erwin r, Rational Rose, ER/Studio, MS Visio, SAP Power designer
• Databases MySQL, SQL Server, Oracle, Hadoop/Hbase, Cassandra, DynamoDB, Azure Table Storage, Natezza
• Reporting Tools MS Office (Word/Excel/Power Point/ Visio), Tableau, Crystal reports XI, SSRS, IBM Cognos7.0/6.0.

Other Requirements

• Eligible to work in the United States (a valid H1B, Green Card or US Citizenship or other type of work visa)
• Work Location – Client site
• Copies of education and technical certifications will be required at the time of interview
• Two professional references will be required before final hiring decision