JetSoftPro

Project Overview:

We are looking for a strong Senior Software Engineer to join a technically challenging project and work within a high-performing engineering environment.

Responsibilities:

Interview Process

The selection process consists of three stages:

1. AI Interview — approximately 20–25 minutes
2. Coding Challenge — candidates must achieve at least 75/100 to pass
3. Technical & Cultural Interview — approximately 30 minutes

• Candidates should be prepared and committed to completing all three stages of the process.
• Accepted Technology Background
• Candidates with strong experience in Python, TypeScript, Go, or JavaScript are welcome to apply. Preference/rubric data experience will be considered an advantage rather than a mandatory requirement.

Skills/Requirements:

• Strong commercial software engineering experience
• Strong proficiency in at least one of the following languages:
• Python
• TypeScript
• Go
• JavaScript
• Strong coding, problem-solving, and software engineering fundamentals
• Experience working with modern development practices and tools
• Experience with preference data, evaluation rubrics, or related AI/ML data workflows is a strong plus, but is not required
• Background at a Tier 1 technology company and/or top-tier university is preferred
• Good English communication skills

Project Description

A B2B marketing analytics platform for LinkedIn advertisers wants to know whether the early signals of an ad campaign can predict its outcome months ahead. Small B2B advertisers close only a handful of deals per year, so standard attribution has too little data to work with. The proof of concept tests a different approach on the platform's historical data across many advertisers. Engagement and website signals from the first 7-21 days of a campaign are combined into a surrogate index that predicts later pipeline outcomes and states how confident the prediction is.

The PoC is time-boxed to 5-6 weeks and ends with a Go or No-Go decision backed by numbers.

Client Description
A B2B marketing analytics platform for LinkedIn

• A B2B marketing analytics platform for LinkedIn advertisers wants to know whether the early signals of an ad campaign can predict its outcome months ahead. Small B2B advertisers close only a handful of deals per year, so standard attribution has too little data to work with. The proof of concept tests a different approach on the platform's historical data across many advertisers. Engagement and website signals from the first 7-21 days of a campaign are combined into a surrogate index that predicts later pipeline outcomes and states how confident the prediction is.
   

Requirements:

• 5+ years of applied data science or statistics at a senior level.
• Strong command of regression modeling, including regularized and hierarchical (multilevel) models.
• Proven experience with small or sparse datasets and with probability calibration.
• Rigorous validation practice: temporal splits, leakage prevention, overfitting control.
• Python (pandas, scikit-learn, statsmodels) and SQL.
• Ability to explain uncertainty to non-technical stakeholders.
• English - upper-intermediate.

Nice to have

• Bayesian tooling such as PyMC, Stan or bambi.
• Familiarity with surrogate index and proxy metric methods, such as the work of Athey, Chetty, Imbens and Kang.
• B2B marketing analytics: attribution, account-based marketing, LinkedIn Ads, CRM pipeline data.
• Holdout design, controlled experiments and sequential testing.
  Statistical process control.
• Part-time, 0.5-0.8 FTE, about 20-32 hours per week.
  5-6 weeks, starting in October 2026, exact date to be confirmed.
• Possible continuation into productization if the PoC succeeds.

Responsibilities:

• Define, together with the client and a business analyst, what counts as campaign success, the outcome window and the cut-off between signals and outcome.
• Design the analytical dataset: candidate signals, outcome labels, exclusion rules and safeguards against future information leaking into the signals. A Python data engineer builds the dataset in ClickHouse to this design.
• Build the surrogate index as a regularized or hierarchical model, with signal weights shared across advertisers and adjusted per advertiser in proportion to its own data volume.
• Validate the model on campaigns it has not seen: temporal backtesting and leave-one-advertiser-out evaluation, with AUC, Brier score and calibration curves.
• Compare the model against the current practice of judging campaigns by CTR and CPC, and measure how prediction quality changes between day 14 and 21.
• Run error analysis, source ablation and learning curves to show which data is missing for a reliable forecast.
• Present weights, patterns and uncertainty to the team and reccomend Go or No-Go for the future phase.
   

We’re looking for a Senior Data Integration Engineer with strong experience in financial services, banking, or fintech to own and scale client data integrations across our platform.

The ideal candidate combines deep software engineering expertise with hands-on experience integrating complex financial systems, including core banking/processing platforms, digital banking solutions, loan origination systems, and CRM platforms.

JetSoftPro is looking for an experienced Senior Data Integration Engineer to join a project for a US-based fintech company working with credit unions and financial institutions.
Position Requirements:

• 8+ years of software engineering experience, including integration work in financial services, banking, or fintech environments 
• Strong command of modern integration technologies: REST APIs, authentication protocols, and data extraction
• Expert-level SQL, understanding of data warehousing
• Azure Fabric Data Warehouse is a plus
• Experience working directly with clients or external technical stakeholders 
• Strong proficiency with AI tools, understanding AI-native software development process
• Bachelor's degree 

• Responsibilities:
  Own the full lifecycle of client data integrations, from discovery and scoping through implementation, automation, and ongoing operation 
• Design and build integrations with credit union technology systems, including core processing, digital banking, loan origination, and CRM platforms
• Partner with client technical teams and third-party vendors to establish access, define data contracts, and establish new connections 
• Own integration reliability, including monitoring, alerting, and issue resolution 
• Handle sensitive financial data in line with security and compliance standards, including encryption, access controls, and auditability 
• Maintain documentation and repeatable patterns so each new integration builds on the last 
• Evaluate build, buy, and partner options for each integration and recommend an approach 
• Contribute improvements to the broader agentic platform beyond integration-specific work, including serving as a key stakeholder on source-system data, contributing to field definitions, mappings, and domain context
• Actively contribute to the development and maintenance company-wide data warehouse