Data Scientist

MAGNiTT, Inc.; Asia; Hybrid; PEN 240,000 - 378,000; Full time

Benefits offered by this job

Hybrid working structure

Remote working policies

Visa & insurance

Leave increment based on tenure

Learning platforms

Career growth

Job summary

MAGNiTT, Inc. in the UAE is seeking a Data Scientist to own Enrich and Signal, translating regional coverage into forward-looking data products and metrics for VC ecosystems.

You will build benchmarks, automate data collection, and apply LLM-assisted extraction, with Python/SQL and a focus on data quality and scalability.

Qualifications

• 3+ years in data science or related roles, preferably with VC/PE exposure.
• Strong Python for data work (pandas/NumPy), scraping and cleaning.
• Advanced SQL with complex joins, window functions, reconciliation across tables.
• Solid grounding in statistics and time-series modeling.
• Experience deriving metrics and proxies (revenues, headcount, valuations).
• Familiarity with fund performance metrics (TVPI, DPI, IRR).
• Experience with entity resolution or record matching.
• Practical experience applying LLMs or AI tools to data extraction and automation.
• Experience building data collection pipelines or scrapers and monitoring checks.
• Product-minded: turning datasets into new metrics and data products.

Responsibilities

• Work with product and leadership to ideate new derived data points, metrics, and computed products from the existing dataset.
• Build the benchmarking engines, starting with MENA fund performance quartiles.
• Own the signal and prediction layer, turning regional coverage into forward-looking data.
• Own entity resolution across the dataset and develop proxies and derived calculations.
• Apply AI and LLM-assisted extraction as the primary collection multiplier to automate workflows.

Skills

Python for data work

SQL

Statistics & modeling

Data scraping & cleaning

LLMs / AI tools

Tools

Python

SQL

Tableau

Pandas

NumPy

Job description

Explore our Pricing!

Data Scientist

United Arab Emirates (UAE) Remote Posted 1 week ago Full-time

About Department

As a data-driven platform, the role of the Data Team at MAGNiTT is to provide quality data around the VC ecosystem in the EVMs, by ensuring accuracy, completeness, reliability, relevance, timeliness, and, accessibility.With a strong focus on data integrity and quality, we aim to leverage our data-driven solutions and support our clients in driving data-based decisions.

Our Benefits

• Hybrid working structure
• Remote working policies
• Visa & insurance
• Leave increment based on tenure
• Learning platforms
• Career growth

Job Description

MAGNiTT's competitive edge rests on the accuracy and completeness of its proprietary dataset, and the Data team plays a crucial role to support it. Every deal, investor profile, and funding round we track becomes the foundation for the intelligence that GPs, LPs, corporates, and governments across MENA, South East Asia, Africa, and other Emerging Venture Markets rely on to source deals, benchmark performance, and report on ecosystem growth

We are looking for a Data Scientist to own Enrich and Signal, the layer of MAGNiTT's data that creates new value rather than maintaining what already exists. You will turn our proprietary regional coverage into products the market cannot get anywhere else. That starts with benchmarking engines such as MENA fund performance quartiles, where global platforms have no coverage. From there, you will build a phased signal and prediction layer that makes our data forward-looking. Alongside this, you will own the automation, entity resolution, and derived metrics that turn raw inputs into analysis-ready data points, so coverage can grow while the collection team stays small.

If you have a strong grounding in Python and SQL, experience building derived metrics or predictive models from imperfect real-world data, and ideally exposure to the VC/PE ecosystem, we should talk about how you might contribute as a Data Scientist at MAGNiTT.

A good candidate will look to do the following:

• Work with product and leadership to ideate new derived data points, metrics, and computed products from the existing dataset.
• Build the benchmarking engines, starting with MENA fund performance quartiles (TVPI, DPI, IRR where derivable). This is the coverage global platforms cannot populate for the region and the clearest thing to sell.
• Own the signal and prediction layer, phased. Turn MAGNiTT’s proprietary regional coverage into forward-looking signal.
• Own entity resolution across the dataset.
• Develop proxies, estimates, and derived calculations (revenue, headcount time series, valuation proxies). Write the formulas, logic, and algorithms that turn raw inputs into higher-quality, analysis-ready data points.
• Apply AI and LLM-assisted extraction as the primary collection multiplier. Convert manual, repetitive analyst workflows into automated processes so the collection team stays small as coverage grows.
• Build and maintain Python (or similar) scripts to automate repetitive data collection and cleaning across MAGNiTT’s core datasets.
• Build collection and enrichment automation and prototypes. Production ingestion, the API, and delivery stay with the engineering team. Anything written to the core dataset conforms to the data quality standard owned by the Data Manager.
• Monitor automated pipelines for errors or drift, and build checks that catch issues before they reach the core dataset.
• Query MAGNiTT’s internal databases (SQL) to test, validate, and refine automation logic and derived metrics.

Job Requirements

• 3+ years in data science, quantitative analysis, or applied data engineering roles, ideally with exposure to VC/PE, private markets, or financial data;
• Strong Python for data work: pandas/NumPy, scraping, parsing, cleaning, and taking a prototype to working automation;
• Advanced SQL: complex joins, window functions, reconciliation across tables, and awareness of query performance;
• Solid grounding in statistics and modeling: estimation under sparse or incomplete data, time series, and building and validating predictive models;
• Experience designing derived metrics and proxies (e.g. revenue, headcount, or valuation estimates) and documenting the logic behind them;
• Familiarity with fund performance metrics (TVPI, DPI, IRR) and how they are calculated from cash flows and valuations, or the ability to get up to speed quickly;
• Hands-on experience with entity resolution or record matching (deduplicating companies, people, or organizations across sources);
• Practical experience applying LLMs or AI tools to data extraction and automation, including evaluating their accuracy;
• Experience building data collection pipelines or scrapers, plus monitoring and checks that catch errors or drift;
• Product-minded: able to turn an existing dataset into new metrics and data products, and to judge which ones customers will pay for;
• Rigorous and systematic, with the judgement to know when an estimate is reliable enough to publish.

Good to have/be

• Experience using LLMs for information extraction or classification, including evaluating their accuracy.
• Experience with data licensing, data partnerships, or due diligence processes.
• Familiarity with the venture capital or private capital ecosystem;
• Experience with Tableau or similar BI tools.

Interview Process

First Interview

A 30-minute video call with the hiring manager, where you can describe your background and highlight your strengths. This is a great opportunity to ask about the specifics of the position.

Technical Assignment

In this stage you'll be given an assignment to work on, to present and discuss it with the hiring manager. Each assignment has been specifically designed for the respective position, and aims to assess your technical skills and thinking process.

Culture Interview

Our culture is a big part of MAGNiTT. With this in mind, our final stage interview is conducted by our employees.

You will meet 3 of our team members, who will assess if you're a good fit for what we're building at MAGNiTT. It's also a great opportunity to ask our employees about how they feel working here.