Senior Data Scientist at Actif.ai

Washington, District of Columbia, United States -

Full Time

Start Date

Immediate

Expiry Date

14 May, 26

Salary

0.0

Posted On

13 Feb, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, SQL, Machine Learning, Experimentation, Reinforcement Learning, Modeling, Feature Engineering, A/B Testing, Simulation, Data Exploration, Communication, Supervised Learning, Decision Logic, Evaluation Frameworks, Statistical Reasoning

Industry

technology;Information and Internet

Description

Actifai is seeking data scientists. The Company Actifai is an artificial intelligence company. We help clients – primarily those in the cable and telecom industry – optimize their high value, high leverage decisions. Our systems support real-world decisions like acquisition, retention, pricing, and upsell by combining machine learning, experimentation, reinforcement learning, and emerging agent-style decision workflows. Actifai is part of Foundry.ai, a technology fund/studio that creates AI software companies in partnership with large global enterprises. Foundry’s operating companies focus on practical applications of AI that drive immediate, measurable, and recurring improvements to financial performance. Foundry is backed by approximately $100MM in capital from leading private equity and venture capital partners. The Position We are seeking a Senior Data Scientist to join our team. In this role, you will help envision, design, code, test, and improve the models and algorithms at the core of our business. This role goes beyond building predictive models, you will help define what should be built, measured, how decisions are evaluated, and how learning systems evolve in production. You will work across modeling, experimentation, and reinforcement-learning–driven decisioning, partnering closely with engineering and product teams to translate data into reliable, high-impact behavior in live applications. As Actifai expands into agent-style decision systems, you will help support the modeling, evaluation, and learning components that inform those systems. Successful candidates are comfortable operating with autonomy, framing sometimes ambiguous business problems, validating assumptions through data exploration, and balancing statistical rigor with practical product constraints in a fast-moving environment. Key Responsibilities Frame and measure decision problems Translate business asks into clearly defined modeling tasks and evaluation frameworks Perform exploratory analysis to assess data quality, label stability, and measurement feasibility before modeling Define success metrics, guardrails, and counterfactual approaches Identify sources of bias or limitations introduced by product and operational flows, agent behavior, or data collection Develop predictive models and decision logic Create and maintain training datasets from multiple production data sources Engineer and validate features with attention to temporal correctness and leakage Train and evaluate supervised learning models for customer decision problems Implement and tune decision logic, including contextual bandit / reinforcement learning approaches layered on predictions Evaluate model and policy performance through offline analysis and simulation Design experiments and evaluate impact Design and analyze A/B tests or staged rollouts Analyze experimental outcomes and translate results into product decisions Diagnose unexpected results, sample issues, or measurement artifacts Partner to operationalize and improve systems Specify model inputs, outputs, and logging requirements needed for monitoring and evaluation Work with engineers to integrate models into live workflows Build dashboards to monitor model and decision performance Investigate anomalies and recommend system improvements Communicate findings and tradeoffs to product, engineering, and client stakeholders Qualifications We are looking for candidates who bring strong applied judgment along with technical depth: 4+ years of experience in applied data science or machine learning Strong Python and SQL skills. Comfortable working with large, messy real-world datasets Experience developing and evaluating supervised ML models Practical experience with experimentation and statistical reasoning Familiarity with reinforcement learning or decisioning frameworks Demonstrated ability to independently frame problems and drive analysis from ambiguity to impact Strong communication skills across technical and business audiences Nice to Have Experience in telecom, subscription, pricing, churn, or retention analytics Exposure to contextual bandits, RL, or policy optimization Experience working in multi-client or product-driven analytics settings Familiarity with AI agents _ Education Degree in a STEM discipline preferred (or equivalent practical experience) Tools and Technologies Our team frequently uses: Python, SQL GitHub, Google Cloud LightGBM, Scikit Learn, PyTorch Docker, Bokeh, BigQuery, FastAPI Benefits and Culture Actifai offers an extremely competitive compensation package, including equity, employer-covered health/vision/dental insurance (with an optional FSA), and 401k matching. Employees receive a generous PTO allowance, and can work both remotely and from our office in downtown Washington, D.C. Successful candidates will join a small team of ambitious, supportive co-workers with ample opportunities to take on responsibilities beyond their assigned role. For additional information on benefits & what it's like to work at Actifai, please visit actif.ai/careers. Finally, we highlight that excellence has no single mold, particularly in a field as rapidly evolving as AI. We're looking for excellent candidates of all backgrounds with strong business intuition and coding skills, and welcome applicants regardless of ethnic/national origin, gender, race, religious beliefs, disability, sexual orientation or age.

Responsibilities

The Senior Data Scientist will be responsible for envisioning, designing, coding, testing, and improving the core models and algorithms, focusing on modeling, experimentation, and reinforcement-learning-driven decisioning. This role involves defining what should be built, how decisions are evaluated, and evolving learning systems in production, partnering with engineering and product teams.