Role: Data Mining Developer
Location: Sunrise, FL – onsite
Duration: 6 Months
Job Description:
The role involves applying data mining, statistical, and machine
learning techniques to solve real-world business problems, particularly in Banking, Financial Services,
and Insurance (BFSI) domains.
· Analyze large structured and semi-structured datasets to identify patterns, trends, and
relationships.
· Apply data mining techniques such as clustering, classification, association rules, and anomaly
detection.
· Build and validate analytical and predictive models to support business decision-making.
· Perform data preparation activities including data cleaning, transformation, and feature
engineer Collaborate with business stakeholders to translate business problems into analytical use cases.
· Work closely with data engineering and data science teams to operationalize insights.
· Develop dashboards, reports, and presentations to communicate insights effectively.
· Ensure data quality, governance, and compliance with enterprise and regulatory standards.
· Explore and apply AI/ML techniques to enhance analytical insights where applicable.”
“We are looking for a Data Mining developer with strong analytical skills to work on large, complex datasets
and uncover actionable business insights. The role involves applying data mining, statistical, and machine
learning techniques to solve real-world business problems, particularly in Banking, Financial Services,
and Insurance (BFSI) domains.
· Analyze large structured and semi-structured datasets to identify patterns, trends, and
relationships.
· Apply data mining techniques such as clustering, classification, association rules, and anomaly
detection.
· Build and validate analytical and predictive models to support business decision-making.
· Perform data preparation activities including data cleaning, transformation, and feature
engineer Collaborate with business stakeholders to translate business problems into analytical use cases.
· Work closely with data engineering and data science teams to operationalize insights.
· Develop dashboards, reports, and presentations to communicate insights effectively.
· Ensure data quality, governance, and compliance with enterprise and regulatory standards.
· Explore and apply AI/ML techniques to enhance analytical insights where applicable.”
Required Skills & Experience : Strong experience in data mining and statistical analysis on large datasets. · Proficiency in SQL for data extraction and analysis. · Hands-on experience with Python and/or R for data analysis and modeling. · Knowledge of data mining and ML algorithms (e.g., decision trees, random forest, regression, clustering). · Experience with data visualization tools (Power BI, Tableau, or similar). · Strong analytical thinking and problem-solving skills. · Ability to work with large datasets in enterprise environments. Preferred / Good-to-Have Skills · Experience in BFSI domain (banking, cards, risk, fraud, customer analytics). · Exposure to Big Data technologies (Spark, Hadoop). · Experience with AI-driven analytics or GenAI-based insight generation. · Familiarity with cloud platforms (GCP). · Understanding of data governance, privacy, and regulatory requirements.
learning techniques to solve real-world business problems, particularly in Banking, Financial Services,
and Insurance (BFSI) domains.
· Analyze large structured and semi-structured datasets to identify patterns, trends, and
relationships.
· Apply data mining techniques such as clustering, classification, association rules, and anomaly
detection.
· Build and validate analytical and predictive models to support business decision-making.
· Perform data preparation activities including data cleaning, transformation, and feature
engineer Collaborate with business stakeholders to translate business problems into analytical use cases.
· Work closely with data engineering and data science teams to operationalize insights.
· Develop dashboards, reports, and presentations to communicate insights effectively.
· Ensure data quality, governance, and compliance with enterprise and regulatory standards.
· Explore and apply AI/ML techniques to enhance analytical insights where applicable.”
“We are looking for a Data Mining developer with strong analytical skills to work on large, complex datasets
and uncover actionable business insights. The role involves applying data mining, statistical, and machine
learning techniques to solve real-world business problems, particularly in Banking, Financial Services,
and Insurance (BFSI) domains.
· Analyze large structured and semi-structured datasets to identify patterns, trends, and
relationships.
· Apply data mining techniques such as clustering, classification, association rules, and anomaly
detection.
· Build and validate analytical and predictive models to support business decision-making.
· Perform data preparation activities including data cleaning, transformation, and feature
engineer Collaborate with business stakeholders to translate business problems into analytical use cases.
· Work closely with data engineering and data science teams to operationalize insights.
· Develop dashboards, reports, and presentations to communicate insights effectively.
· Ensure data quality, governance, and compliance with enterprise and regulatory standards.
· Explore and apply AI/ML techniques to enhance analytical insights where applicable.”
Required Skills & Experience : Strong experience in data mining and statistical analysis on large datasets. · Proficiency in SQL for data extraction and analysis. · Hands-on experience with Python and/or R for data analysis and modeling. · Knowledge of data mining and ML algorithms (e.g., decision trees, random forest, regression, clustering). · Experience with data visualization tools (Power BI, Tableau, or similar). · Strong analytical thinking and problem-solving skills. · Ability to work with large datasets in enterprise environments. Preferred / Good-to-Have Skills · Experience in BFSI domain (banking, cards, risk, fraud, customer analytics). · Exposure to Big Data technologies (Spark, Hadoop). · Experience with AI-driven analytics or GenAI-based insight generation. · Familiarity with cloud platforms (GCP). · Understanding of data governance, privacy, and regulatory requirements.
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