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Senior Data Modeler, Fraud Risk Detection

Bangun model deteksi penipuan untuk perusahaan global

Anda akan mengembangkan model deteksi penipuan dan fitur yang mengidentifikasi aktivitas berisiko tinggi sambil meminimalkan gangguan untuk pelanggan sah. Kerja sama dengan data scientist senior dan insinyur untuk memecahkan masalah, mengembangkan fitur, dan menguji model. Anda akan bekerja dengan dataset kompleks untuk menemukan pola penipuan dan menerapkan model ke dalam produksi.

Kenapa Menarik?

Bekerja dengan tim senior dan mempengaruhi solusi deteksi penipuan di perusahaan global

Tanggung Jawab Utama

  • Mengidentifikasi pola penipuan, metode serangan, dan sinyal perilaku dari dataset kompleks
  • Membangun model machine learning untuk deteksi penipuan di berbagai skenario
  • Menilai model menggunakan metrik teknis dan bisnis seperti presisi, recall, dan tingkat penangkapan penipuan
  • Mengembangkan dan memvalidasi fitur menggunakan berbagai sumber data
  • Menulis kode yang bersih dan teruji, serta bekerja dengan tim engineering untuk memproduksi model

Persyaratan

  • Pendidikan S1 atau lebih tinggi di bidang ilmu komputer, statistik, atau bidang kuantitatif terkait
  • Pengalaman 1+ tahun di bidang data science, machine learning, atau modeling statistik
  • Kemampuan berkomunikasi dengan baik kepada audiens teknis dan non-teknis
  • Kemampuan untuk menerapkan prinsip teoritis menjadi solusi yang siap produksi

Skills Wajib

machine-learningdata-analysisstatistical-modelingpythonsql

Konteks Indonesia

Overlap Jam Kerja:
Fleksibel — atur jam kerjamu sendiri
Lihat selisih gaji remote (USD) vs lokal →
Lihat Deskripsi Asli dari SmartRecruitersTampilkan selengkapnya

Deskripsi asli dari SmartRecruiters

Overview Experian's Fraud Analytics & Commercialization operates across four main functions. These include client engagement analytics, scalable and custom analytics for financial institutions, fraud analytics consulting, and solution integrity and enablement for production-ready platforms. We're looking for a motivated Data Scientist to help build fraud detection models and features that identify high-risk activity while minimizing friction for legitimate customers. Core skills for this role include strategic thinking, an eagerness to collaborate, and empathy. You will will dig into surprising signals in the data and to learn how that insight becomes a deployed model. You will help investigate the latest fraud patterns, build features, and train and evaluate machine learning models. You will work with senior data scientists and engineers starting with problem definition through feature engineering, experimentation, and deployment. You will be a developing programmer, ready to translate theoretical principles into production-ready solutions. We continue to sharpen through research and the engineering that turns those findings into tools and systems built for commercialization. This is a remote role and you will report into the Sr. Manager of Fraud Analytics. What you'll do Explore complex datasets, with guidance from senior team members, to identify fraud patterns, attack methods, and behavioral signals. Work with senior data scientists to translate fraud questions into testable hypotheses Help build machine learning models for fraud detection across account opening, account takeover, and identity risk. Evaluate models using both technical and business metrics, such as precision, recall, fraud capture rate, false-positive rate, and customer friction. Develop and validate features using identity, transactional, behavioral, and other available data sources. Write clean, well-tested code, and work with engineering to bring models and features into production. Partner with the score monitoring team to help set up model and feature monitoring, and support research on related client questions. Help prepare analyses and communicate findings to both technical and nontechnical audiences. Apply Experian's standards for data privacy, model documentation, explainability, validation, and governance. 1+ years of experience in data science, machine learning, statistical modeling, or a related quantitative field Bachelor's or advanced degree in computer science, statistics, mathematics, economics, engineering, data science, or another quantitative discipline Foundation in supervised learning, model evaluation, feature selection, statistical inference, and techniques such as classification and anomaly detection. Proficiency in Python, with the ability to write clean, readable, and well-tested code. Familiarity with common data science and machine-learning tools such as pandas, NumPy, and scikit-learn. Investigative mindset and the ability to move from unusual data patterns to testable hypotheses. Familiarity with PySpark, cloud platforms such as Amazon Web Services, Google Cloud, Azure, Databricks, and Snowflake, or other large-scale data tools. Exposure to financial services, FinTech, payments, or another regulated or fraud-intensive industry, through coursework, internship, or prior work. #LI-Remote Benefits/Perks: Great compensation package and bonus plan Core benefits including medical, dental, vision, and matching 401K Flexible work environment, ability to work remote, hybrid or in-office Flexible time off including volunteer time off, vacation, sick and 12-paid holidays Explore all our exciting benefits here: https://yourexperianbenefits.com/cand-index.html Our uniqueness is that we celebrate yours. Experian's people first, inclusive and purpose-driven culture is multi award-winning. We have won World's Best Workplaces™ 2025 (Fortune Global Top 25), and Great Place To Work™ in 26 countries to name a few. Experian's recruitment process is conducted only through authorised channels. Recruitment communications will only be sent from an @ experian.com email address. Experian will never ask candidates to make any payment as part of an application, interview, assessment, onboarding, or recruitment process. Our compensation reflects the cost of labor across several U.S. geographic markets. Within this range, individual pay is determined by work location and additional factors such as job-related skills, experience, and education. You will be eligible for a variable pay opportunity and a comprehensive benefits package. Experian is proud to be an Equal Opportunity Employer for all groups protected under applicable federal, state and local law, including protected veterans and individuals with disabilities. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity.

Konteks Gaji

Posisi Data & Analytics serupa di LokerDollar dibayar sekitar $156.5k/yr (kisaran $32k–262.8k/yr, dari 74 listing aktif).

Perekrutan di Experian

Experian punya 86 lowongan aktif lain di LokerDollar dan telah merekrut di sini sejak 27 Mar 2026 — di kategori Data & Analytics, Operations, Engineering.

Lihat semua lowongan Experian →

Konteks pasar

  • TERVERIFIKASIGaji tercantum 28% di bawah median peran serupa $156,500/tahun (n=74 listing bergaji).
  • TERVERIFIKASIExperian: 187 lowongan dalam 3 bulan terakhir, 280 sepanjang waktu di LokerDollar.
  • TERVERIFIKASIPerusahaan pertama kali terlihat 27 Mar 2026.
  • TERVERIFIKASILowongan ini pertama kali terlihat 26 Sep 2026.
  • TERVERIFIKASITerakhir diverifikasi masih aktif 26 Sep 2026.

Pemberi kerja tidak menyatakan keterbukaan lokasi — cek langsung lowongannya

Perusahaan
Experian
Gaji
$82.6k–143.2k/yr
Tipe Lowongan
full time
Lokasi
Dublin, Ireland · Remote
Level
mid
DipostingNewBaru & terverifikasi
25 Sep 2026

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Pertanyaan yang sering diajukan

Apakah Senior Data Modeler, Fraud Risk Detection di Experian bisa dikerjakan remote?
Ya, Senior Data Modeler, Fraud Risk Detection di Experian bisa dikerjakan remote, tetapi perusahaan tidak menyebutkan negara mana saja yang boleh melamar. Cek deskripsi lowongan sebelum melamar.
Berapa gaji untuk Senior Data Modeler, Fraud Risk Detection di Experian?
Rentang gaji yang tercantum untuk posisi ini adalah $82.6k–143.2k/yr.
Jenis pekerjaan apa Senior Data Modeler, Fraud Risk Detection di Experian?
Posisi ini adalah pekerjaan full time.
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