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Hiring a Freelance Machine Learning Engineer

Why and when you need a Machine Learning Engineer?

The main goal of a ML expert is to help companies understand, define, evaluate and implement machine learning projects to add value to the organization.

Our machine learning freelancers develop projects across multiple areas of the business and clients can focus on specific use cases if needed; being able to cover the whole project cycle:

  • Initial check of the predictive capacity of the data.
  • Data processing and enrichment.
  • Definition of the ue case objective and evaluation methods.
  • Definition of the project approach.
  • Execution of the project and development of machine learning models.
  • Rollout of models into production.
  • Integration and adoption in business processes.

The main goal of a ML expert is to help companies understand, define, evaluate and implement machine learning projects to add value to the organization.

Such effort can impact many areas of an organization, with the potential to improve many of the KPIs of the company, increasing income and reducing costs.

Why does your company need an expert in machine learning?

Across many internal operational activities or customer facing processes and interactions, the types of use cases in your company that can benefit from deploying machine learning are multiple. Performance improvements that can result in increased income, reduced costs, less customer leakage, increased sales, reduced incidents and trouble tickets, early fraud detection, identify purchase patterns, predict system failures, etc.

Why is it better to have an outsourced machine learning expert?

It is all about rolling out proven use cases! Expert freelancers that have done it before and can share their experience from multiple industries or business areas provide the best source for learning fast and creating impact. The combination of experienced freelancers and your own teams is very effective to ramp-up knowledge and capabilities in your organization.

Data-driven decision-making is a great way to gain a competitive advantage, increase profits and reduce costs!

Case studies with Machine learning engineer

Certified Machine learning engineers in the Network

case study
Machine learning case study for an Insurance company to detect fraud

Challenge, Context, Problems to be solved

Fraud detection in the commercial network

Mission, tools and methodology

Fraud detection: internal, of customers, of suppliers, associated branches ...

Fraud patterns were detected optimizing the cost with respect to the income for each unit studied.

Achieved results

Reduction of costs derived from fraud.

Francesc
Computer Science and Engineering

case study
Machine learning use case for a Debt collection software powered by AI

Challenge, Context, Problems to be solved

Detection of customer behaviors to achieve greater recovery.

Mission, tools and methodology

Collections optimization with ML Python

The key variables to increase the recovery rate in clients were detected. Strengthening and improving them thanks to a strategy of continuous improvement on the results of the analytical engine.

Customers have also been segmented according to behavior. Own data has been integrated with 3rd party data. A BI reporting has been created with Tableau to improve internal knowledge and give more value of the product to the end customer.

Achieved results

Recovery KPI increase

Francesc
Computer Science and Engineering

case study
Machine learning use case for a leading provider of Information solutions on users behaviour

Challenge, Context, Problems to be solved

Detection of buying patterns and recommenders.

Mission, tools and methodology

Buying patterns of certain books, groups ... were detected to be applied in all countries through the commercial network. In addition to an assistant for the commercial network to understand what customers needed according to their profile.

Achieved results

Understand customers to offer just what they need.

Francesc
Computer Science and Engineering

case study
Machine learning case study for an AdTech company to detect fraud

Challenge, Context, Problems to be solved

Detection of fraud in online ads

Mission, tools and methodology

Customers were found to be using ads fraudulently to earn revenue from their apps.

Operational and financial data were cross-checked to quickly detect who was committing fraud and what patterns they were using. Used Redshift, Python & Knime.

Achieved results

Reduction of costs derived from fraud.

Francesc
Computer Science and Engineering

case study
Machine learning use case for a chain of Supermarkets to increase customer loyalty

Challenge, Context, Problems to be solved

Increase customer loyalty

Mission, tools and methodology

Thanks to the purchase data, patterns were detected to generate offers that increased customer loyalty.

Achieved results

A double benefit is achieved, on one hand increasing customer loyalty ratios.

And on the other hand, increase sales thanks to the understanding of the customer, the detection of behavior patterns and the optimization of offers.

Francesc
Computer Science and Engineering

case study
Machine learning use case for a Sports center management company to reduce churn

Challenge, Context, Problems to be solved

Reduce the number of people cancelling their memberships

Mission, tools and methodology

Detect churn patterns, areas with churn increases, profiling, customer assessment, recommend actions

Achieved results

Reduction of churn.

Greater benefits are achieved by understanding the client, adapting the offer to the demand, understanding the client's life cycle, optimizing prices / offers, detecting problems in the low / high.

Francesc
Computer Science and Engineering

case study
Machine learning use case for an Airline company to manage revenue with Alteryx

Challenge, Context, Problems to be solved

Manage and optimize revenues.

Mission, tools and methodology

Optimize costs and increase the benefits derived from overbooking, planning, routes, etc.

Achieved results

Analysts were empowered to optimize costs and increase the benefits derived from overbooking, planning, routes, etc.

Francesc
Computer Science and Engineering

case study
Machine learning use case for a Broadcast media

Challenge, Context, Problems to be solved

Conversation detection

Mission, tools and methodology

Analyze conversations on different platforms and social networks about the contents of the CCMA to detect trends, ideas, news ...

Achieved results

Increased understanding of the audience.

Francesc
Computer Science and Engineering

case study
Machine learning use case for a furniture Retail

Challenge, Context, Problems to be solved

Marketing campaign optimization.

Mission, tools and methodology

Detection of customers around the stores and which offers worked best with what type of customers.

Achieved results

Higher return on investment in marketing.

Francesc
Computer Science and Engineering

case study
Machine learning use case for a Financial services company to reduce non payments

Challenge, Context, Problems to be solved

Avoid non payments and recover unpaids.

Mission, tools and methodology

Analyzed behavior patterns to detect when customers were to become outstanding. Detect what actions to take to recover unpaid clients.

Achieved results

Improvement in non payment rates and better customer service.

Francesc
Computer Science and Engineering

Must have Machine learning Skills

Machine learning engineers must have a complete set of capabilities and experience:

  • Data analytics & machine learning strategic consulting.
  • Good Business Analysis experience
  • Development, management & implementation of business analytics projects.
  • Capacity to choose the technologies to use and architecture to implement.
  • Innovation based on data (products, services, etc).
  • With a broad functional view and use case exposure: improve commercial offer, improve recruitment, reduce customer cancellations, help locate new stores, reduce costs, cross-selling, up-selling, fraud detection, etc.

Other skills needed:

  • Business intelligence
  • Business analytics
  • Sales
  • Partner relationship management
  • Data science & machine learning
  • Cloud & distribution computing
  • Business development
  • Relationship management
  • Data engineering & DW
  • Big data

The technical knowledge of a data analyst should include the command of various of the following solutions, frameworks and languages:

  • Snowflake · AWS · Google Cloud/AI · MS Azure · IBM Watson · Oracle · Hadoop · SAS · Splunk · Kubernetes · SAP Hana · Elastic · Salesforce
  • Qlikview · Tableau · Alteryx · Trifacta . Power BI · Google Analytics
  • Python · Java · R · Spark · SQL · MQL

Responsibilities of this role

End-to-end responsibility in the implementation of the data analytics project.

Very versatile profile based on real needs in data analytics projects, where the key is to achieve an impact on the business aligned with the objectives and its strategy. This requires understanding the needs of the client (internal or external), how to apply the analytics of data to meet the defined goals and how to achieve this in the minimum time & cost and with 100% guarantees.

Challenges and hot topics for Machine learning engineers

The main cross-cutting challenges for all companies would be:

  • Customer analytics (analysis of customer information): Market analysis, sales optimization, improved understanding / relationship with the customer and prediction of customer behavior (additions / deletions / changes ...).
  • Operations Analytics: Analysis of the supply chain (supply, production, warehouse management, transport and distribution at the point of sale) and the new applications linked to the Internet of Things (IOT) and geographic information systems.
  • People analytics (talent within the organization): Analysis for effective strategic management of human resources, so that business objectives can be met quickly and efficiently, obtaining optimal performance on human capital.